Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = '/data'
!pip install matplotlib==2.0.2
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Collecting matplotlib==2.0.2
  Downloading https://files.pythonhosted.org/packages/60/d4/6b6d8a7a6bc69a1602ab372f6fc6e88ef88a8a96398a1a25edbac636295b/matplotlib-2.0.2-cp36-cp36m-manylinux1_x86_64.whl (14.6MB)
    100% |████████████████████████████████| 14.6MB 47kB/s  eta 0:00:01
Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.6/site-packages/cycler-0.10.0-py3.6.egg (from matplotlib==2.0.2)
Requirement already satisfied: pytz in /opt/conda/lib/python3.6/site-packages (from matplotlib==2.0.2)
Requirement already satisfied: python-dateutil in /opt/conda/lib/python3.6/site-packages (from matplotlib==2.0.2)
Requirement already satisfied: pyparsing!=2.0.0,!=2.0.4,!=2.1.2,!=2.1.6,>=1.5.6 in /opt/conda/lib/python3.6/site-packages (from matplotlib==2.0.2)
Requirement already satisfied: six>=1.10 in /opt/conda/lib/python3.6/site-packages (from matplotlib==2.0.2)
Requirement already satisfied: numpy>=1.7.1 in /opt/conda/lib/python3.6/site-packages (from matplotlib==2.0.2)
Installing collected packages: matplotlib
  Found existing installation: matplotlib 2.1.0
    Uninstalling matplotlib-2.1.0:
      Successfully uninstalled matplotlib-2.1.0
Successfully installed matplotlib-2.0.2
You are using pip version 9.0.1, however version 10.0.1 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f1442b88ef0>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f1442a74ba8>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.3.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    real_input = tf.placeholder(dtype=tf.float32,shape=(None,image_width,image_height,image_channels),name='real_input')
    z_input = tf.placeholder(dtype=tf.float32,shape=(None,z_dim),name='z_input')
    learning_rate = tf.placeholder(dtype=tf.float32,name='learning_rate')
    return real_input,z_input,learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
ERROR:tensorflow:==================================
Object was never used (type <class 'tensorflow.python.framework.ops.Operation'>):
<tf.Operation 'assert_rank_2/Assert/Assert' type=Assert>
If you want to mark it as used call its "mark_used()" method.
It was originally created here:
['File "/opt/conda/lib/python3.6/runpy.py", line 193, in _run_module_as_main\n    "__main__", mod_spec)', 'File "/opt/conda/lib/python3.6/runpy.py", line 85, in _run_code\n    exec(code, run_globals)', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py", line 16, in <module>\n    app.launch_new_instance()', 'File "/opt/conda/lib/python3.6/site-packages/traitlets/config/application.py", line 658, in launch_instance\n    app.start()', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel/kernelapp.py", line 478, in start\n    self.io_loop.start()', 'File "/opt/conda/lib/python3.6/site-packages/zmq/eventloop/ioloop.py", line 177, in start\n    super(ZMQIOLoop, self).start()', 'File "/opt/conda/lib/python3.6/site-packages/tornado/ioloop.py", line 888, in start\n    handler_func(fd_obj, events)', 'File "/opt/conda/lib/python3.6/site-packages/tornado/stack_context.py", line 277, in null_wrapper\n    return fn(*args, **kwargs)', 'File "/opt/conda/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events\n    self._handle_recv()', 'File "/opt/conda/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv\n    self._run_callback(callback, msg)', 'File "/opt/conda/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback\n    callback(*args, **kwargs)', 'File "/opt/conda/lib/python3.6/site-packages/tornado/stack_context.py", line 277, in null_wrapper\n    return fn(*args, **kwargs)', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 281, in dispatcher\n    return self.dispatch_shell(stream, msg)', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 232, in dispatch_shell\n    handler(stream, idents, msg)', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 397, in execute_request\n    user_expressions, allow_stdin)', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel/ipkernel.py", line 208, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel/zmqshell.py", line 533, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)', 'File "/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2728, in run_cell\n    interactivity=interactivity, compiler=compiler, result=result)', 'File "/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2856, in run_ast_nodes\n    if self.run_code(code, result):', 'File "/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2910, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)', 'File "<ipython-input-5-dc8c1ae98567>", line 22, in <module>\n    tests.test_model_inputs(model_inputs)', 'File "/home/workspace/face_generation/problem_unittests.py", line 12, in func_wrapper\n    result = func(*args)', 'File "/home/workspace/face_generation/problem_unittests.py", line 68, in test_model_inputs\n    _check_input(learn_rate, [], \'Learning Rate\')', 'File "/home/workspace/face_generation/problem_unittests.py", line 34, in _check_input\n    _assert_tensor_shape(tensor, shape, \'Real Input\')', 'File "/home/workspace/face_generation/problem_unittests.py", line 20, in _assert_tensor_shape\n    assert tf.assert_rank(tensor, len(shape), message=\'{} has wrong rank\'.format(display_name))', 'File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/check_ops.py", line 617, in assert_rank\n    dynamic_condition, data, summarize)', 'File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/check_ops.py", line 571, in _assert_rank_condition\n    return control_flow_ops.Assert(condition, data, summarize=summarize)', 'File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py", line 175, in wrapped\n    return _add_should_use_warning(fn(*args, **kwargs))', 'File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py", line 144, in _add_should_use_warning\n    wrapped = TFShouldUseWarningWrapper(x)', 'File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py", line 101, in __init__\n    stack = [s.strip() for s in traceback.format_stack()]']
==================================
Tests Passed
In [6]:
def lrelu(x,alpha=0.1):
    return tf.maximum(alpha*x,x)

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [8]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    #input size : 28*28*3
    alpha = 0.2
    with tf.variable_scope('discriminator',reuse=reuse):
        
        conv1 = tf.layers.conv2d(inputs=images,filters=128,strides=2,kernel_size=5,padding='same',activation=None,kernel_initializer=tf.contrib.layers.xavier_initializer())
        #bn1 = tf.layers.batch_normalization(inputs=conv1,training=True,epsilon=1e-5,momentum=0.9)
        lrelu1 = lrelu(conv1,alpha) #14*14*128
        
        conv2 = tf.layers.conv2d(inputs=lrelu1,filters=256,strides=2,kernel_size=5,padding='same',activation=None,kernel_initializer=tf.contrib.layers.xavier_initializer())#tf.random_normal_initializer(mean=0.0, stddev=0.02)) 
        bn2 = tf.layers.batch_normalization(inputs=conv2,training=True,epsilon=1e-5,momentum=0.9)
        lrelu2 = lrelu(bn2,alpha) #7*7*256
        
        conv3 = tf.layers.conv2d(inputs=lrelu2,filters=512,strides=2,kernel_size=5,padding='same',activation=None,kernel_initializer=tf.contrib.layers.xavier_initializer())#tf.random_normal_initializer(mean=0.0, stddev=0.02)) 
        bn3 = tf.layers.batch_normalization(inputs=conv3,training=True,epsilon=1e-5,momentum=0.9)
        lrelu3 = lrelu(bn3,alpha)#4*4*512
        
        flatten = tf.reshape(lrelu3,shape=(-1,4*4*512))
        logits = tf.layers.dense(inputs=flatten,units=1,activation=None,kernel_initializer=tf.contrib.layers.xavier_initializer())#tf.random_normal_initializer(mean=0.0, stddev=0.02))
        outputs = tf.nn.sigmoid(logits)
        
    #output size : None,1
    return outputs,logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [9]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    reuse = not is_train
    # TODO: Implement Function
    with tf.variable_scope('generator',reuse=reuse):
        fc1 = tf.layers.dense(inputs=z,units=7*7*512)
        fc1 = tf.reshape(fc1,(-1,7,7,512))
        fc1 = tf.layers.batch_normalization(inputs=fc1,training=is_train,epsilon=1e-5,momentum=0.9)
        fc1 = lrelu(fc1)  #7*7*512
        
        tconv1 = tf.layers.conv2d_transpose(inputs=fc1,filters=256,strides=1,kernel_size=5,padding='same',activation=None,kernel_initializer=tf.random_normal_initializer(mean=0.0, stddev=0.02))
        bn1 = tf.layers.batch_normalization(inputs=tconv1,training=is_train,epsilon=1e-5,momentum=0.9)
        lrelu1 = lrelu(bn1) #7*7*256
        
        tconv2 = tf.layers.conv2d_transpose(inputs=fc1,filters=128,strides=2,kernel_size=5,padding='same',activation=None,kernel_initializer=tf.random_normal_initializer(mean=0.0, stddev=0.02))
        bn2 = tf.layers.batch_normalization(inputs=tconv2,training=is_train,epsilon=1e-5,momentum=0.9)
        lrelu1 = lrelu(bn2) #14*14*128
        
        logits = tf.layers.conv2d_transpose(inputs=lrelu1,filters=out_channel_dim,strides=2,kernel_size=5,padding='same',activation=None,kernel_initializer=tf.random_normal_initializer(mean=0.0, stddev=0.02))
        outputs = tf.nn.tanh(logits) #28*28*3
        
    return outputs


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [10]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    label_smoothness = 0.1
    
    g_model = generator(z=input_z,out_channel_dim=out_channel_dim,is_train=True)
    d_real_outputs,d_real_logits = discriminator(images=input_real,reuse=False)
    d_fake_outputs,d_fake_logits = discriminator(images=g_model,reuse=True)
    
    #d_loss_real : 
    d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_real_logits,labels=tf.ones_like(d_real_logits)*(1-label_smoothness)))
    
    #d_loss_fake :
    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_fake_logits,labels=tf.zeros_like(d_fake_logits)))
    
    #g_loss : 
    gloss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_fake_logits,labels=tf.ones_like(d_fake_logits)))
    
    dloss = d_loss_real + d_loss_fake
    
    return dloss, gloss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [11]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    #get weights and bias of the networks separately
    t_vars = tf.trainable_variables()
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    
    '''
    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
    with tf.control_dependencies(update_ops):
        # Ensures that we execute the update_ops before performing the train_step
        train_step = tf.train.GradientDescentOptimizer(0.01).minimize(loss)
    '''
    #here batch normalization is the operation
    # Because the batch norm layers are not part of the graph we inforce these operation to run before the 
    # optimizers so the batch normalization layers can update their population statistics.
    
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        g_op = tf.train.AdamOptimizer(learning_rate=learning_rate,beta1=beta1).minimize(g_loss,var_list=g_vars)
        d_op = tf.train.AdamOptimizer(learning_rate=learning_rate,beta1=beta1).minimize(d_loss,var_list=d_vars)

    return d_op,g_op


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [12]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [13]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    _,image_width,image_height,image_channels = data_shape
    real_input,z_input,lr = model_inputs(image_width, image_height, image_channels, z_dim)
    d_loss,g_loss = model_loss(real_input, z_input, image_channels)
    op_d,op_g = model_opt(d_loss, g_loss, lr, beta1)
    steps=0
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                steps+=1
                
                batch_z = np.random.uniform(low=-1,high=1,size=(batch_size,z_dim))
                
                #training discriminator network first
                _ = sess.run(op_d,feed_dict={real_input:batch_images,
                                                     z_input:batch_z,
                                                     lr: learning_rate
                                                     })
                
                #training generator network
                _ = sess.run(op_g,feed_dict={real_input:batch_images,
                                                     z_input:batch_z,
                                                     lr: learning_rate
                                                     })
                
                #show generator output
                if steps%100==0:
                    show_generator_output(sess,50,z_input,image_channels,data_image_mode)
                
                if steps%10==0:
                    dloss_train = sess.run(d_loss,feed_dict={real_input:batch_images,z_input:batch_z})
                    gloss_train = sess.run(g_loss,feed_dict={z_input:batch_z})
                    print("Epoch:{}, Step:{}, Discriminator Loss:{}, Generator Loss:{}".format(epoch_i+1,steps,dloss_train,gloss_train))                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [14]:
batch_size = 25
z_dim = 100
learning_rate = 0.0005
beta1 = 0.2


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch:1, Step:10, Discriminator Loss:1.820533037185669, Generator Loss:3.9409122467041016
Epoch:1, Step:20, Discriminator Loss:0.9148613810539246, Generator Loss:1.3422045707702637
Epoch:1, Step:30, Discriminator Loss:2.0818750858306885, Generator Loss:0.2641730308532715
Epoch:1, Step:40, Discriminator Loss:2.273115634918213, Generator Loss:0.19567298889160156
Epoch:1, Step:50, Discriminator Loss:3.0575788021087646, Generator Loss:4.501502513885498
Epoch:1, Step:60, Discriminator Loss:1.9475407600402832, Generator Loss:1.3409268856048584
Epoch:1, Step:70, Discriminator Loss:1.7366983890533447, Generator Loss:0.9101427793502808
Epoch:1, Step:80, Discriminator Loss:1.80427885055542, Generator Loss:1.362085223197937
Epoch:1, Step:90, Discriminator Loss:1.9894928932189941, Generator Loss:1.6737194061279297
Epoch:1, Step:100, Discriminator Loss:1.417389988899231, Generator Loss:0.920832097530365
Epoch:1, Step:110, Discriminator Loss:1.6370601654052734, Generator Loss:1.4611142873764038
Epoch:1, Step:120, Discriminator Loss:1.484775424003601, Generator Loss:1.6522666215896606
Epoch:1, Step:130, Discriminator Loss:1.522895097732544, Generator Loss:0.7014479637145996
Epoch:1, Step:140, Discriminator Loss:1.9759960174560547, Generator Loss:2.573930263519287
Epoch:1, Step:150, Discriminator Loss:1.213870644569397, Generator Loss:1.3520268201828003
Epoch:1, Step:160, Discriminator Loss:1.5882833003997803, Generator Loss:0.3993661105632782
Epoch:1, Step:170, Discriminator Loss:2.369194746017456, Generator Loss:0.1701553910970688
Epoch:1, Step:180, Discriminator Loss:2.0894954204559326, Generator Loss:0.2419431209564209
Epoch:1, Step:190, Discriminator Loss:0.9148098230361938, Generator Loss:0.9466684460639954
Epoch:1, Step:200, Discriminator Loss:1.1350069046020508, Generator Loss:0.8429010510444641
Epoch:1, Step:210, Discriminator Loss:0.7498580813407898, Generator Loss:1.5631996393203735
Epoch:1, Step:220, Discriminator Loss:1.0480672121047974, Generator Loss:4.156437873840332
Epoch:1, Step:230, Discriminator Loss:1.1334511041641235, Generator Loss:3.794229507446289
Epoch:1, Step:240, Discriminator Loss:0.6153780817985535, Generator Loss:1.618513822555542
Epoch:1, Step:250, Discriminator Loss:0.43588167428970337, Generator Loss:2.717115879058838
Epoch:1, Step:260, Discriminator Loss:0.36651843786239624, Generator Loss:3.805495262145996
Epoch:1, Step:270, Discriminator Loss:1.0107903480529785, Generator Loss:0.9580705761909485
Epoch:1, Step:280, Discriminator Loss:1.2747831344604492, Generator Loss:1.153182029724121
Epoch:1, Step:290, Discriminator Loss:1.1801728010177612, Generator Loss:1.0988867282867432
Epoch:1, Step:300, Discriminator Loss:0.953209638595581, Generator Loss:1.8610529899597168
Epoch:1, Step:310, Discriminator Loss:1.078442931175232, Generator Loss:1.8861825466156006
Epoch:1, Step:320, Discriminator Loss:1.3072469234466553, Generator Loss:0.6308732032775879
Epoch:1, Step:330, Discriminator Loss:1.8884401321411133, Generator Loss:0.33594340085983276
Epoch:1, Step:340, Discriminator Loss:2.597236156463623, Generator Loss:0.14741353690624237
Epoch:1, Step:350, Discriminator Loss:1.6320788860321045, Generator Loss:0.42764103412628174
Epoch:1, Step:360, Discriminator Loss:2.2795472145080566, Generator Loss:0.18011736869812012
Epoch:1, Step:370, Discriminator Loss:1.816861867904663, Generator Loss:0.3079800009727478
Epoch:1, Step:380, Discriminator Loss:0.8357925415039062, Generator Loss:1.088416576385498
Epoch:1, Step:390, Discriminator Loss:0.5640501976013184, Generator Loss:2.658473014831543
Epoch:1, Step:400, Discriminator Loss:1.6557425260543823, Generator Loss:0.37259235978126526
Epoch:1, Step:410, Discriminator Loss:0.950563907623291, Generator Loss:1.767866849899292
Epoch:1, Step:420, Discriminator Loss:1.0502665042877197, Generator Loss:0.8693041801452637
Epoch:1, Step:430, Discriminator Loss:0.763776421546936, Generator Loss:1.4369606971740723
Epoch:1, Step:440, Discriminator Loss:1.3699272871017456, Generator Loss:2.9832706451416016
Epoch:1, Step:450, Discriminator Loss:0.7308555841445923, Generator Loss:1.2433054447174072
Epoch:1, Step:460, Discriminator Loss:0.47895288467407227, Generator Loss:2.3171095848083496
Epoch:1, Step:470, Discriminator Loss:0.6374068260192871, Generator Loss:1.5893529653549194
Epoch:1, Step:480, Discriminator Loss:0.4400581121444702, Generator Loss:3.908034324645996
Epoch:1, Step:490, Discriminator Loss:0.5507129430770874, Generator Loss:1.8373427391052246
Epoch:1, Step:500, Discriminator Loss:1.287418246269226, Generator Loss:0.8463798761367798
Epoch:1, Step:510, Discriminator Loss:1.938234567642212, Generator Loss:0.29630887508392334
Epoch:1, Step:520, Discriminator Loss:2.4268994331359863, Generator Loss:0.17617277801036835
Epoch:1, Step:530, Discriminator Loss:0.9115856885910034, Generator Loss:0.9718091487884521
Epoch:1, Step:540, Discriminator Loss:2.154820203781128, Generator Loss:0.29556089639663696
Epoch:1, Step:550, Discriminator Loss:1.2933495044708252, Generator Loss:0.5985087156295776
Epoch:1, Step:560, Discriminator Loss:0.5282982587814331, Generator Loss:1.9761420488357544
Epoch:1, Step:570, Discriminator Loss:0.5339226722717285, Generator Loss:1.9123387336730957
Epoch:1, Step:580, Discriminator Loss:0.388940691947937, Generator Loss:3.2953600883483887
Epoch:1, Step:590, Discriminator Loss:0.44264405965805054, Generator Loss:2.8023359775543213
Epoch:1, Step:600, Discriminator Loss:0.4056798219680786, Generator Loss:2.7935986518859863
Epoch:1, Step:610, Discriminator Loss:4.574822425842285, Generator Loss:6.025956153869629
Epoch:1, Step:620, Discriminator Loss:0.8241658806800842, Generator Loss:1.1214280128479004
Epoch:1, Step:630, Discriminator Loss:0.7107356190681458, Generator Loss:1.395384430885315
Epoch:1, Step:640, Discriminator Loss:0.6793098449707031, Generator Loss:1.4071974754333496
Epoch:1, Step:650, Discriminator Loss:0.8265817165374756, Generator Loss:1.043729543685913
Epoch:1, Step:660, Discriminator Loss:0.5216769576072693, Generator Loss:1.9485111236572266
Epoch:1, Step:670, Discriminator Loss:0.5088571310043335, Generator Loss:2.048279047012329
Epoch:1, Step:680, Discriminator Loss:3.045186996459961, Generator Loss:0.10295325517654419
Epoch:1, Step:690, Discriminator Loss:0.5059070587158203, Generator Loss:2.054622173309326
Epoch:1, Step:700, Discriminator Loss:0.5645793676376343, Generator Loss:1.7922403812408447
Epoch:1, Step:710, Discriminator Loss:0.3870561718940735, Generator Loss:3.576042890548706
Epoch:1, Step:720, Discriminator Loss:0.3612988293170929, Generator Loss:3.8724989891052246
Epoch:1, Step:730, Discriminator Loss:0.36972591280937195, Generator Loss:3.8934316635131836
Epoch:1, Step:740, Discriminator Loss:2.515791177749634, Generator Loss:0.22182981669902802
Epoch:1, Step:750, Discriminator Loss:1.2612816095352173, Generator Loss:0.599644660949707
Epoch:1, Step:760, Discriminator Loss:0.5172932147979736, Generator Loss:2.355860710144043
Epoch:1, Step:770, Discriminator Loss:2.8064308166503906, Generator Loss:0.1387939751148224
Epoch:1, Step:780, Discriminator Loss:0.4901038408279419, Generator Loss:2.282508134841919
Epoch:1, Step:790, Discriminator Loss:0.46021199226379395, Generator Loss:2.4812092781066895
Epoch:1, Step:800, Discriminator Loss:0.4190720319747925, Generator Loss:3.0129432678222656
Epoch:1, Step:810, Discriminator Loss:0.36208707094192505, Generator Loss:4.147546768188477
Epoch:1, Step:820, Discriminator Loss:0.34724161028862, Generator Loss:4.615452766418457
Epoch:1, Step:830, Discriminator Loss:0.34741050004959106, Generator Loss:4.271139144897461
Epoch:1, Step:840, Discriminator Loss:0.38401147723197937, Generator Loss:4.582093238830566
Epoch:1, Step:850, Discriminator Loss:0.3596571981906891, Generator Loss:4.060551643371582
Epoch:1, Step:860, Discriminator Loss:0.3571799695491791, Generator Loss:3.8453528881073
Epoch:1, Step:870, Discriminator Loss:0.3458080589771271, Generator Loss:4.591634273529053
Epoch:1, Step:880, Discriminator Loss:0.33664390444755554, Generator Loss:5.428379535675049
Epoch:1, Step:890, Discriminator Loss:0.48864856362342834, Generator Loss:4.91024923324585
Epoch:1, Step:900, Discriminator Loss:0.33541157841682434, Generator Loss:5.62169885635376
Epoch:1, Step:910, Discriminator Loss:3.0865414142608643, Generator Loss:0.12665827572345734
Epoch:1, Step:920, Discriminator Loss:0.7042235136032104, Generator Loss:1.2915983200073242
Epoch:1, Step:930, Discriminator Loss:0.5056201815605164, Generator Loss:1.9518475532531738
Epoch:1, Step:940, Discriminator Loss:2.9991822242736816, Generator Loss:6.015738487243652
Epoch:1, Step:950, Discriminator Loss:0.48415160179138184, Generator Loss:2.2698819637298584
Epoch:1, Step:960, Discriminator Loss:1.0750970840454102, Generator Loss:0.7825309634208679
Epoch:1, Step:970, Discriminator Loss:0.6475723385810852, Generator Loss:1.635664701461792
Epoch:1, Step:980, Discriminator Loss:0.5076683759689331, Generator Loss:2.013249397277832
Epoch:1, Step:990, Discriminator Loss:0.3908262252807617, Generator Loss:3.032148838043213
Epoch:1, Step:1000, Discriminator Loss:0.4675743281841278, Generator Loss:2.205878734588623
Epoch:1, Step:1010, Discriminator Loss:0.3773708939552307, Generator Loss:3.6259231567382812
Epoch:1, Step:1020, Discriminator Loss:0.3722512423992157, Generator Loss:3.430410861968994
Epoch:1, Step:1030, Discriminator Loss:0.3455551564693451, Generator Loss:4.522198677062988
Epoch:1, Step:1040, Discriminator Loss:0.3461400270462036, Generator Loss:4.991184234619141
Epoch:1, Step:1050, Discriminator Loss:0.363692045211792, Generator Loss:3.961681365966797
Epoch:1, Step:1060, Discriminator Loss:0.33575963973999023, Generator Loss:5.531917572021484
Epoch:1, Step:1070, Discriminator Loss:0.36399945616722107, Generator Loss:3.6551718711853027
Epoch:1, Step:1080, Discriminator Loss:0.3504375219345093, Generator Loss:4.5159149169921875
Epoch:1, Step:1090, Discriminator Loss:0.3379170000553131, Generator Loss:5.589547634124756
Epoch:1, Step:1100, Discriminator Loss:0.44439762830734253, Generator Loss:3.459942102432251
Epoch:1, Step:1110, Discriminator Loss:0.3380308151245117, Generator Loss:4.811801910400391
Epoch:1, Step:1120, Discriminator Loss:0.3435128927230835, Generator Loss:4.386746406555176
Epoch:1, Step:1130, Discriminator Loss:0.3436489701271057, Generator Loss:5.052177906036377
Epoch:1, Step:1140, Discriminator Loss:0.34269094467163086, Generator Loss:4.524632453918457
Epoch:1, Step:1150, Discriminator Loss:0.3580174744129181, Generator Loss:4.1048431396484375
Epoch:1, Step:1160, Discriminator Loss:0.35321906208992004, Generator Loss:5.107526779174805
Epoch:1, Step:1170, Discriminator Loss:0.3400830328464508, Generator Loss:4.7046217918396
Epoch:1, Step:1180, Discriminator Loss:1.3319143056869507, Generator Loss:0.8589252233505249
Epoch:1, Step:1190, Discriminator Loss:0.3367404639720917, Generator Loss:5.480226993560791
Epoch:1, Step:1200, Discriminator Loss:0.8734790682792664, Generator Loss:2.5798161029815674
Epoch:1, Step:1210, Discriminator Loss:0.6411081552505493, Generator Loss:1.5705790519714355
Epoch:1, Step:1220, Discriminator Loss:0.4287145733833313, Generator Loss:2.5508017539978027
Epoch:1, Step:1230, Discriminator Loss:0.3824326992034912, Generator Loss:3.295863151550293
Epoch:1, Step:1240, Discriminator Loss:0.4157528877258301, Generator Loss:2.950017213821411
Epoch:1, Step:1250, Discriminator Loss:1.8141478300094604, Generator Loss:3.4600892066955566
Epoch:1, Step:1260, Discriminator Loss:0.5473853349685669, Generator Loss:2.361884593963623
Epoch:1, Step:1270, Discriminator Loss:0.4604399800300598, Generator Loss:2.359492778778076
Epoch:1, Step:1280, Discriminator Loss:0.5071786642074585, Generator Loss:1.9468151330947876
Epoch:1, Step:1290, Discriminator Loss:0.38705697655677795, Generator Loss:3.0538511276245117
Epoch:1, Step:1300, Discriminator Loss:0.35343503952026367, Generator Loss:3.986680507659912
Epoch:1, Step:1310, Discriminator Loss:2.6468210220336914, Generator Loss:7.259467124938965
Epoch:1, Step:1320, Discriminator Loss:0.9899702072143555, Generator Loss:0.9859706163406372
Epoch:1, Step:1330, Discriminator Loss:0.4851089417934418, Generator Loss:2.127833127975464
Epoch:1, Step:1340, Discriminator Loss:0.37645184993743896, Generator Loss:3.3334693908691406
Epoch:1, Step:1350, Discriminator Loss:3.0549023151397705, Generator Loss:5.038796424865723
Epoch:1, Step:1360, Discriminator Loss:1.9733837842941284, Generator Loss:0.35932889580726624
Epoch:1, Step:1370, Discriminator Loss:0.5007234811782837, Generator Loss:2.1001811027526855
Epoch:1, Step:1380, Discriminator Loss:0.3767806887626648, Generator Loss:3.539750576019287
Epoch:1, Step:1390, Discriminator Loss:0.39475032687187195, Generator Loss:3.1056981086730957
Epoch:1, Step:1400, Discriminator Loss:0.3519412875175476, Generator Loss:4.533136367797852
Epoch:1, Step:1410, Discriminator Loss:0.8187052011489868, Generator Loss:3.869380235671997
Epoch:1, Step:1420, Discriminator Loss:1.3680050373077393, Generator Loss:0.6917670965194702
Epoch:1, Step:1430, Discriminator Loss:1.0232582092285156, Generator Loss:1.1176011562347412
Epoch:1, Step:1440, Discriminator Loss:0.4255448877811432, Generator Loss:2.634659767150879
Epoch:1, Step:1450, Discriminator Loss:0.4313080310821533, Generator Loss:2.5697689056396484
Epoch:1, Step:1460, Discriminator Loss:0.3526952564716339, Generator Loss:4.226070404052734
Epoch:1, Step:1470, Discriminator Loss:0.4337235987186432, Generator Loss:2.465395450592041
Epoch:1, Step:1480, Discriminator Loss:0.4162790775299072, Generator Loss:4.039025783538818
Epoch:1, Step:1490, Discriminator Loss:0.410164475440979, Generator Loss:4.228982448577881
Epoch:1, Step:1500, Discriminator Loss:0.34058064222335815, Generator Loss:5.1357293128967285
Epoch:1, Step:1510, Discriminator Loss:0.3381148874759674, Generator Loss:6.018165588378906
Epoch:1, Step:1520, Discriminator Loss:0.3384813070297241, Generator Loss:5.182836532592773
Epoch:1, Step:1530, Discriminator Loss:0.3304020166397095, Generator Loss:5.889750003814697
Epoch:1, Step:1540, Discriminator Loss:0.854222297668457, Generator Loss:1.6855840682983398
Epoch:1, Step:1550, Discriminator Loss:0.48236364126205444, Generator Loss:2.318248748779297
Epoch:1, Step:1560, Discriminator Loss:0.5413374900817871, Generator Loss:2.048966646194458
Epoch:1, Step:1570, Discriminator Loss:0.44548800587654114, Generator Loss:2.771265745162964
Epoch:1, Step:1580, Discriminator Loss:0.3637021780014038, Generator Loss:4.106144905090332
Epoch:1, Step:1590, Discriminator Loss:0.339372456073761, Generator Loss:5.304697036743164
Epoch:1, Step:1600, Discriminator Loss:0.34741315245628357, Generator Loss:4.729931831359863
Epoch:1, Step:1610, Discriminator Loss:0.3526309132575989, Generator Loss:4.17230749130249
Epoch:1, Step:1620, Discriminator Loss:0.3900279700756073, Generator Loss:3.780189275741577
Epoch:1, Step:1630, Discriminator Loss:0.33793097734451294, Generator Loss:5.150960922241211
Epoch:1, Step:1640, Discriminator Loss:0.35008934140205383, Generator Loss:5.371021270751953
Epoch:1, Step:1650, Discriminator Loss:0.3393283188343048, Generator Loss:5.403895378112793
Epoch:1, Step:1660, Discriminator Loss:0.45645463466644287, Generator Loss:2.9084558486938477
Epoch:1, Step:1670, Discriminator Loss:0.33910804986953735, Generator Loss:5.201613426208496
Epoch:1, Step:1680, Discriminator Loss:0.33342868089675903, Generator Loss:5.386868476867676
Epoch:1, Step:1690, Discriminator Loss:0.3585509657859802, Generator Loss:3.8007795810699463
Epoch:1, Step:1700, Discriminator Loss:0.3396529257297516, Generator Loss:6.149774074554443
Epoch:1, Step:1710, Discriminator Loss:0.33011695742607117, Generator Loss:6.236284255981445
Epoch:1, Step:1720, Discriminator Loss:0.3447325825691223, Generator Loss:6.391504287719727
Epoch:1, Step:1730, Discriminator Loss:0.34800273180007935, Generator Loss:5.957099437713623
Epoch:1, Step:1740, Discriminator Loss:0.3357333242893219, Generator Loss:5.1517462730407715
Epoch:1, Step:1750, Discriminator Loss:0.3307895362377167, Generator Loss:6.090943336486816
Epoch:1, Step:1760, Discriminator Loss:0.3621998429298401, Generator Loss:6.064459323883057
Epoch:1, Step:1770, Discriminator Loss:0.49616947770118713, Generator Loss:3.344758987426758
Epoch:1, Step:1780, Discriminator Loss:1.7209279537200928, Generator Loss:3.5461325645446777
Epoch:1, Step:1790, Discriminator Loss:2.376436710357666, Generator Loss:0.2324141561985016
Epoch:1, Step:1800, Discriminator Loss:0.5701007843017578, Generator Loss:1.8387818336486816
Epoch:1, Step:1810, Discriminator Loss:0.39985573291778564, Generator Loss:2.9114913940429688
Epoch:1, Step:1820, Discriminator Loss:0.45135796070098877, Generator Loss:2.4286158084869385
Epoch:1, Step:1830, Discriminator Loss:0.43713098764419556, Generator Loss:4.127497673034668
Epoch:1, Step:1840, Discriminator Loss:0.8650661706924438, Generator Loss:1.9919207096099854
Epoch:1, Step:1850, Discriminator Loss:0.618437647819519, Generator Loss:2.3585453033447266
Epoch:1, Step:1860, Discriminator Loss:0.5564240217208862, Generator Loss:1.820049524307251
Epoch:1, Step:1870, Discriminator Loss:0.41271165013313293, Generator Loss:3.041205644607544
Epoch:1, Step:1880, Discriminator Loss:0.3556818664073944, Generator Loss:3.9937362670898438
Epoch:1, Step:1890, Discriminator Loss:0.3631259500980377, Generator Loss:3.954745292663574
Epoch:1, Step:1900, Discriminator Loss:0.3459484577178955, Generator Loss:5.628599166870117
Epoch:1, Step:1910, Discriminator Loss:0.7514007091522217, Generator Loss:1.5598785877227783
Epoch:1, Step:1920, Discriminator Loss:0.6785469055175781, Generator Loss:2.0159215927124023
Epoch:1, Step:1930, Discriminator Loss:0.43509963154792786, Generator Loss:2.7080864906311035
Epoch:1, Step:1940, Discriminator Loss:0.6964309215545654, Generator Loss:1.3501980304718018
Epoch:1, Step:1950, Discriminator Loss:0.7760043740272522, Generator Loss:2.284769058227539
Epoch:1, Step:1960, Discriminator Loss:0.4327983856201172, Generator Loss:2.6581430435180664
Epoch:1, Step:1970, Discriminator Loss:0.5209225416183472, Generator Loss:2.026899576187134
Epoch:1, Step:1980, Discriminator Loss:0.3493320345878601, Generator Loss:4.218500137329102
Epoch:1, Step:1990, Discriminator Loss:0.3527868986129761, Generator Loss:4.042992115020752
Epoch:1, Step:2000, Discriminator Loss:0.37697282433509827, Generator Loss:3.3625879287719727
Epoch:1, Step:2010, Discriminator Loss:0.484887033700943, Generator Loss:3.649722099304199
Epoch:1, Step:2020, Discriminator Loss:0.416880339384079, Generator Loss:2.6900148391723633
Epoch:1, Step:2030, Discriminator Loss:0.3391540050506592, Generator Loss:5.31265926361084
Epoch:1, Step:2040, Discriminator Loss:0.39563313126564026, Generator Loss:3.6021575927734375
Epoch:1, Step:2050, Discriminator Loss:0.33184748888015747, Generator Loss:5.804635047912598
Epoch:1, Step:2060, Discriminator Loss:0.3618181645870209, Generator Loss:3.8431079387664795
Epoch:1, Step:2070, Discriminator Loss:0.3539552092552185, Generator Loss:4.491515159606934
Epoch:1, Step:2080, Discriminator Loss:0.33579492568969727, Generator Loss:5.551514148712158
Epoch:1, Step:2090, Discriminator Loss:0.3348143994808197, Generator Loss:5.335618019104004
Epoch:1, Step:2100, Discriminator Loss:0.35280707478523254, Generator Loss:4.670098781585693
Epoch:1, Step:2110, Discriminator Loss:0.4351712763309479, Generator Loss:3.2705063819885254
Epoch:1, Step:2120, Discriminator Loss:0.3430592715740204, Generator Loss:4.724702835083008
Epoch:1, Step:2130, Discriminator Loss:0.4624254107475281, Generator Loss:4.0423688888549805
Epoch:1, Step:2140, Discriminator Loss:0.3446558713912964, Generator Loss:4.5752739906311035
Epoch:1, Step:2150, Discriminator Loss:0.3375445306301117, Generator Loss:5.051849365234375
Epoch:1, Step:2160, Discriminator Loss:0.40580442547798157, Generator Loss:3.694467306137085
Epoch:1, Step:2170, Discriminator Loss:0.33573976159095764, Generator Loss:7.5066094398498535
Epoch:1, Step:2180, Discriminator Loss:0.3577045500278473, Generator Loss:3.8911399841308594
Epoch:1, Step:2190, Discriminator Loss:3.7044179439544678, Generator Loss:7.591641902923584
Epoch:1, Step:2200, Discriminator Loss:0.6913775205612183, Generator Loss:1.9020053148269653
Epoch:1, Step:2210, Discriminator Loss:0.49960917234420776, Generator Loss:2.5852551460266113
Epoch:1, Step:2220, Discriminator Loss:0.5148928165435791, Generator Loss:2.627352237701416
Epoch:1, Step:2230, Discriminator Loss:0.41856592893600464, Generator Loss:2.9896726608276367
Epoch:1, Step:2240, Discriminator Loss:0.4497728645801544, Generator Loss:2.558241844177246
Epoch:1, Step:2250, Discriminator Loss:0.4030132293701172, Generator Loss:3.65562105178833
Epoch:1, Step:2260, Discriminator Loss:0.3533748388290405, Generator Loss:5.759008884429932
Epoch:1, Step:2270, Discriminator Loss:0.37197673320770264, Generator Loss:4.95796012878418
Epoch:1, Step:2280, Discriminator Loss:0.36014148592948914, Generator Loss:5.251466751098633
Epoch:1, Step:2290, Discriminator Loss:0.35476887226104736, Generator Loss:5.249920845031738
Epoch:1, Step:2300, Discriminator Loss:0.3783469796180725, Generator Loss:4.3388752937316895
Epoch:1, Step:2310, Discriminator Loss:0.34683024883270264, Generator Loss:5.02003812789917
Epoch:1, Step:2320, Discriminator Loss:2.119833469390869, Generator Loss:0.4097994863986969
Epoch:1, Step:2330, Discriminator Loss:0.5577552318572998, Generator Loss:2.073323965072632
Epoch:1, Step:2340, Discriminator Loss:0.3737636208534241, Generator Loss:3.696331262588501
Epoch:1, Step:2350, Discriminator Loss:0.3482836186885834, Generator Loss:4.291164398193359
Epoch:1, Step:2360, Discriminator Loss:2.4018149375915527, Generator Loss:0.3043229579925537
Epoch:1, Step:2370, Discriminator Loss:0.6602938175201416, Generator Loss:1.5882607698440552
Epoch:1, Step:2380, Discriminator Loss:0.39576566219329834, Generator Loss:3.166334629058838
Epoch:1, Step:2390, Discriminator Loss:0.3772098422050476, Generator Loss:3.220669984817505
Epoch:1, Step:2400, Discriminator Loss:0.36924663186073303, Generator Loss:3.436708927154541
Epoch:2, Step:2410, Discriminator Loss:0.8491042256355286, Generator Loss:2.271116256713867
Epoch:2, Step:2420, Discriminator Loss:1.8967435359954834, Generator Loss:0.48700645565986633
Epoch:2, Step:2430, Discriminator Loss:0.46015465259552, Generator Loss:2.4394915103912354
Epoch:2, Step:2440, Discriminator Loss:0.5217223167419434, Generator Loss:2.2008941173553467
Epoch:2, Step:2450, Discriminator Loss:0.4696100652217865, Generator Loss:2.178135871887207
Epoch:2, Step:2460, Discriminator Loss:0.3953426480293274, Generator Loss:3.9902868270874023
Epoch:2, Step:2470, Discriminator Loss:0.848536491394043, Generator Loss:1.0543043613433838
Epoch:2, Step:2480, Discriminator Loss:0.34366506338119507, Generator Loss:4.843794822692871
Epoch:2, Step:2490, Discriminator Loss:0.3588011562824249, Generator Loss:3.7086527347564697
Epoch:2, Step:2500, Discriminator Loss:0.8414300680160522, Generator Loss:1.5419433116912842
Epoch:2, Step:2510, Discriminator Loss:2.134927749633789, Generator Loss:5.586821556091309
Epoch:2, Step:2520, Discriminator Loss:0.48004603385925293, Generator Loss:2.3870162963867188
Epoch:2, Step:2530, Discriminator Loss:0.4022626280784607, Generator Loss:2.974681854248047
Epoch:2, Step:2540, Discriminator Loss:0.44101452827453613, Generator Loss:2.8811111450195312
Epoch:2, Step:2550, Discriminator Loss:0.3477039039134979, Generator Loss:4.191671371459961
Epoch:2, Step:2560, Discriminator Loss:0.3408549726009369, Generator Loss:5.066245079040527
Epoch:2, Step:2570, Discriminator Loss:0.6264134645462036, Generator Loss:1.5945593118667603
Epoch:2, Step:2580, Discriminator Loss:0.3560846447944641, Generator Loss:3.8597195148468018
Epoch:2, Step:2590, Discriminator Loss:0.33603498339653015, Generator Loss:4.991721153259277
Epoch:2, Step:2600, Discriminator Loss:0.35069209337234497, Generator Loss:4.529960632324219
Epoch:2, Step:2610, Discriminator Loss:0.3460836708545685, Generator Loss:5.292983531951904
Epoch:2, Step:2620, Discriminator Loss:0.3670516312122345, Generator Loss:4.171331882476807
Epoch:2, Step:2630, Discriminator Loss:0.35070815682411194, Generator Loss:3.969587802886963
Epoch:2, Step:2640, Discriminator Loss:0.33535394072532654, Generator Loss:5.092077255249023
Epoch:2, Step:2650, Discriminator Loss:0.3554762899875641, Generator Loss:3.78690767288208
Epoch:2, Step:2660, Discriminator Loss:0.3623494505882263, Generator Loss:4.6425933837890625
Epoch:2, Step:2670, Discriminator Loss:0.5921318531036377, Generator Loss:2.499116897583008
Epoch:2, Step:2680, Discriminator Loss:0.42142871022224426, Generator Loss:3.7483160495758057
Epoch:2, Step:2690, Discriminator Loss:0.34856897592544556, Generator Loss:4.399563312530518
Epoch:2, Step:2700, Discriminator Loss:0.34980762004852295, Generator Loss:4.047887325286865
Epoch:2, Step:2710, Discriminator Loss:0.3947192430496216, Generator Loss:3.002530574798584
Epoch:2, Step:2720, Discriminator Loss:0.3683851361274719, Generator Loss:3.7058937549591064
Epoch:2, Step:2730, Discriminator Loss:0.3377770781517029, Generator Loss:4.825159072875977
Epoch:2, Step:2740, Discriminator Loss:0.3380054831504822, Generator Loss:4.676164627075195
Epoch:2, Step:2750, Discriminator Loss:0.3446725010871887, Generator Loss:5.583766937255859
Epoch:2, Step:2760, Discriminator Loss:0.33820340037345886, Generator Loss:4.964598655700684
Epoch:2, Step:2770, Discriminator Loss:0.3772708773612976, Generator Loss:5.512443542480469
Epoch:2, Step:2780, Discriminator Loss:3.317056655883789, Generator Loss:0.1784074604511261
Epoch:2, Step:2790, Discriminator Loss:0.8295642137527466, Generator Loss:1.1181458234786987
Epoch:2, Step:2800, Discriminator Loss:0.3955197036266327, Generator Loss:3.150247573852539
Epoch:2, Step:2810, Discriminator Loss:3.565709114074707, Generator Loss:0.10226033627986908
Epoch:2, Step:2820, Discriminator Loss:0.5066927075386047, Generator Loss:2.4084882736206055
Epoch:2, Step:2830, Discriminator Loss:0.5200040936470032, Generator Loss:2.0188283920288086
Epoch:2, Step:2840, Discriminator Loss:0.35727354884147644, Generator Loss:3.8625681400299072
Epoch:2, Step:2850, Discriminator Loss:0.38028115034103394, Generator Loss:3.6240615844726562
Epoch:2, Step:2860, Discriminator Loss:0.39654749631881714, Generator Loss:3.066141128540039
Epoch:2, Step:2870, Discriminator Loss:0.34269142150878906, Generator Loss:4.932718753814697
Epoch:2, Step:2880, Discriminator Loss:0.3869248032569885, Generator Loss:3.7870583534240723
Epoch:2, Step:2890, Discriminator Loss:0.3480473458766937, Generator Loss:4.25262451171875
Epoch:2, Step:2900, Discriminator Loss:1.1721293926239014, Generator Loss:1.0119976997375488
Epoch:2, Step:2910, Discriminator Loss:0.4068446755409241, Generator Loss:2.957859992980957
Epoch:2, Step:2920, Discriminator Loss:0.4085923135280609, Generator Loss:2.786853313446045
Epoch:2, Step:2930, Discriminator Loss:0.3603798747062683, Generator Loss:3.7004570960998535
Epoch:2, Step:2940, Discriminator Loss:0.43060076236724854, Generator Loss:3.209799289703369
Epoch:2, Step:2950, Discriminator Loss:0.4042735695838928, Generator Loss:3.5479302406311035
Epoch:2, Step:2960, Discriminator Loss:0.3719134032726288, Generator Loss:3.4920310974121094
Epoch:2, Step:2970, Discriminator Loss:0.3424438238143921, Generator Loss:4.8903656005859375
Epoch:2, Step:2980, Discriminator Loss:0.3485886752605438, Generator Loss:4.120173454284668
Epoch:2, Step:2990, Discriminator Loss:0.3362548351287842, Generator Loss:4.934674263000488
Epoch:2, Step:3000, Discriminator Loss:0.36309289932250977, Generator Loss:3.7590548992156982
Epoch:2, Step:3010, Discriminator Loss:0.34430983662605286, Generator Loss:5.21846342086792
Epoch:2, Step:3020, Discriminator Loss:0.33448871970176697, Generator Loss:5.767599105834961
Epoch:2, Step:3030, Discriminator Loss:0.3466079533100128, Generator Loss:4.455410480499268
Epoch:2, Step:3040, Discriminator Loss:0.3371579945087433, Generator Loss:5.117435455322266
Epoch:2, Step:3050, Discriminator Loss:0.632659912109375, Generator Loss:1.5495128631591797
Epoch:2, Step:3060, Discriminator Loss:4.152669429779053, Generator Loss:6.294779300689697
Epoch:2, Step:3070, Discriminator Loss:1.7840726375579834, Generator Loss:0.5014454126358032
Epoch:2, Step:3080, Discriminator Loss:0.4043247401714325, Generator Loss:3.0528769493103027
Epoch:2, Step:3090, Discriminator Loss:0.4758472740650177, Generator Loss:2.3424770832061768
Epoch:2, Step:3100, Discriminator Loss:0.44221746921539307, Generator Loss:2.781851053237915
Epoch:2, Step:3110, Discriminator Loss:0.3902742862701416, Generator Loss:4.379969120025635
Epoch:2, Step:3120, Discriminator Loss:0.3405078947544098, Generator Loss:5.139349937438965
Epoch:2, Step:3130, Discriminator Loss:0.769932746887207, Generator Loss:3.0930566787719727
Epoch:2, Step:3140, Discriminator Loss:0.3973357677459717, Generator Loss:3.4795594215393066
Epoch:2, Step:3150, Discriminator Loss:1.0468957424163818, Generator Loss:0.8157903552055359
Epoch:2, Step:3160, Discriminator Loss:0.37672311067581177, Generator Loss:3.316599130630493
Epoch:2, Step:3170, Discriminator Loss:0.39931827783584595, Generator Loss:3.0364394187927246
Epoch:2, Step:3180, Discriminator Loss:0.38247889280319214, Generator Loss:4.346619129180908
Epoch:2, Step:3190, Discriminator Loss:0.35064104199409485, Generator Loss:4.482108116149902
Epoch:2, Step:3200, Discriminator Loss:0.34230056405067444, Generator Loss:4.978360652923584
Epoch:2, Step:3210, Discriminator Loss:0.35207894444465637, Generator Loss:4.508276462554932
Epoch:2, Step:3220, Discriminator Loss:0.3542134761810303, Generator Loss:4.195794105529785
Epoch:2, Step:3230, Discriminator Loss:1.2712037563323975, Generator Loss:0.709375262260437
Epoch:2, Step:3240, Discriminator Loss:0.3468957543373108, Generator Loss:5.254576683044434
Epoch:2, Step:3250, Discriminator Loss:0.41992664337158203, Generator Loss:2.6922802925109863
Epoch:2, Step:3260, Discriminator Loss:0.3412191867828369, Generator Loss:4.707362174987793
Epoch:2, Step:3270, Discriminator Loss:0.33584654331207275, Generator Loss:6.1402997970581055
Epoch:2, Step:3280, Discriminator Loss:0.3834826648235321, Generator Loss:3.49910569190979
Epoch:2, Step:3290, Discriminator Loss:0.3412703573703766, Generator Loss:4.837090492248535
Epoch:2, Step:3300, Discriminator Loss:0.3750152587890625, Generator Loss:3.7886579036712646
Epoch:2, Step:3310, Discriminator Loss:0.361373633146286, Generator Loss:4.661067008972168
Epoch:2, Step:3320, Discriminator Loss:0.5158389210700989, Generator Loss:2.8637795448303223
Epoch:2, Step:3330, Discriminator Loss:0.3559790253639221, Generator Loss:4.878878593444824
Epoch:2, Step:3340, Discriminator Loss:0.33246561884880066, Generator Loss:5.76793098449707
Epoch:2, Step:3350, Discriminator Loss:0.3328666388988495, Generator Loss:5.330129623413086
Epoch:2, Step:3360, Discriminator Loss:0.3673296272754669, Generator Loss:4.627403259277344
Epoch:2, Step:3370, Discriminator Loss:0.34845033288002014, Generator Loss:4.534366607666016
Epoch:2, Step:3380, Discriminator Loss:0.33097511529922485, Generator Loss:7.076030731201172
Epoch:2, Step:3390, Discriminator Loss:0.34728923439979553, Generator Loss:4.858585834503174
Epoch:2, Step:3400, Discriminator Loss:0.35315006971359253, Generator Loss:3.8401217460632324
Epoch:2, Step:3410, Discriminator Loss:0.34995126724243164, Generator Loss:5.370983600616455
Epoch:2, Step:3420, Discriminator Loss:0.3312922418117523, Generator Loss:5.669468879699707
Epoch:2, Step:3430, Discriminator Loss:0.3440372943878174, Generator Loss:6.633655548095703
Epoch:2, Step:3440, Discriminator Loss:0.334384024143219, Generator Loss:6.567496299743652
Epoch:2, Step:3450, Discriminator Loss:0.37382742762565613, Generator Loss:3.2782154083251953
Epoch:2, Step:3460, Discriminator Loss:0.3384609818458557, Generator Loss:4.780501365661621
Epoch:2, Step:3470, Discriminator Loss:0.34974032640457153, Generator Loss:4.136346817016602
Epoch:2, Step:3480, Discriminator Loss:0.3620620369911194, Generator Loss:5.331597328186035
Epoch:2, Step:3490, Discriminator Loss:0.33609288930892944, Generator Loss:5.202762603759766
Epoch:2, Step:3500, Discriminator Loss:0.3298132121562958, Generator Loss:6.538034439086914
Epoch:2, Step:3510, Discriminator Loss:0.329274445772171, Generator Loss:6.620176315307617
Epoch:2, Step:3520, Discriminator Loss:0.35669293999671936, Generator Loss:6.6190080642700195
Epoch:2, Step:3530, Discriminator Loss:0.3298943042755127, Generator Loss:5.922781467437744
Epoch:2, Step:3540, Discriminator Loss:3.08549165725708, Generator Loss:0.2834080159664154
Epoch:2, Step:3550, Discriminator Loss:1.566906213760376, Generator Loss:0.4210706353187561
Epoch:2, Step:3560, Discriminator Loss:0.7940583825111389, Generator Loss:1.8792839050292969
Epoch:2, Step:3570, Discriminator Loss:0.6310117840766907, Generator Loss:2.4634170532226562
Epoch:2, Step:3580, Discriminator Loss:0.5484839677810669, Generator Loss:2.0800580978393555
Epoch:2, Step:3590, Discriminator Loss:0.5126318335533142, Generator Loss:2.064952850341797
Epoch:2, Step:3600, Discriminator Loss:1.379258394241333, Generator Loss:0.5988131761550903
Epoch:2, Step:3610, Discriminator Loss:1.0065979957580566, Generator Loss:1.1098601818084717
Epoch:2, Step:3620, Discriminator Loss:0.5483571290969849, Generator Loss:1.9453556537628174
Epoch:2, Step:3630, Discriminator Loss:0.4208044707775116, Generator Loss:2.6277101039886475
Epoch:2, Step:3640, Discriminator Loss:0.3740951120853424, Generator Loss:3.487502098083496
Epoch:2, Step:3650, Discriminator Loss:0.3786819875240326, Generator Loss:3.7799243927001953
Epoch:2, Step:3660, Discriminator Loss:0.3823579251766205, Generator Loss:3.208162307739258
Epoch:2, Step:3670, Discriminator Loss:0.41971760988235474, Generator Loss:3.0992178916931152
Epoch:2, Step:3680, Discriminator Loss:0.4783709645271301, Generator Loss:2.153904676437378
Epoch:2, Step:3690, Discriminator Loss:0.5056505799293518, Generator Loss:3.262772560119629
Epoch:2, Step:3700, Discriminator Loss:0.3569369912147522, Generator Loss:5.272477149963379
Epoch:2, Step:3710, Discriminator Loss:0.4391889274120331, Generator Loss:4.319665431976318
Epoch:2, Step:3720, Discriminator Loss:1.0034397840499878, Generator Loss:1.9559162855148315
Epoch:2, Step:3730, Discriminator Loss:1.1113548278808594, Generator Loss:3.7729837894439697
Epoch:2, Step:3740, Discriminator Loss:0.467971533536911, Generator Loss:2.1757216453552246
Epoch:2, Step:3750, Discriminator Loss:0.4278593063354492, Generator Loss:2.7888004779815674
Epoch:2, Step:3760, Discriminator Loss:0.4077519178390503, Generator Loss:2.818495273590088
Epoch:2, Step:3770, Discriminator Loss:0.4421941936016083, Generator Loss:2.658535957336426
Epoch:2, Step:3780, Discriminator Loss:0.3689464330673218, Generator Loss:5.21181583404541
Epoch:2, Step:3790, Discriminator Loss:3.8251399993896484, Generator Loss:0.15152877569198608
Epoch:2, Step:3800, Discriminator Loss:2.0254878997802734, Generator Loss:0.3015388250350952
Epoch:2, Step:3810, Discriminator Loss:0.44581809639930725, Generator Loss:3.669276714324951
Epoch:2, Step:3820, Discriminator Loss:0.48872655630111694, Generator Loss:2.238600730895996
Epoch:2, Step:3830, Discriminator Loss:0.3947347104549408, Generator Loss:3.311457633972168
Epoch:2, Step:3840, Discriminator Loss:0.7625654339790344, Generator Loss:1.5410256385803223
Epoch:2, Step:3850, Discriminator Loss:0.4035432040691376, Generator Loss:3.080695152282715
Epoch:2, Step:3860, Discriminator Loss:0.34695035219192505, Generator Loss:4.730655670166016
Epoch:2, Step:3870, Discriminator Loss:0.3597520887851715, Generator Loss:3.900378465652466
Epoch:2, Step:3880, Discriminator Loss:0.38887694478034973, Generator Loss:4.480120658874512
Epoch:2, Step:3890, Discriminator Loss:0.34723156690597534, Generator Loss:4.3039984703063965
Epoch:2, Step:3900, Discriminator Loss:0.3594484329223633, Generator Loss:4.775671005249023
Epoch:2, Step:3910, Discriminator Loss:0.4218660593032837, Generator Loss:4.253329277038574
Epoch:2, Step:3920, Discriminator Loss:0.46812885999679565, Generator Loss:2.741112470626831
Epoch:2, Step:3930, Discriminator Loss:0.3321317434310913, Generator Loss:5.578618049621582
Epoch:2, Step:3940, Discriminator Loss:0.8060508966445923, Generator Loss:2.3636109828948975
Epoch:2, Step:3950, Discriminator Loss:0.6086568832397461, Generator Loss:2.0376598834991455
Epoch:2, Step:3960, Discriminator Loss:0.6562387943267822, Generator Loss:1.5564136505126953
Epoch:2, Step:3970, Discriminator Loss:0.4084364175796509, Generator Loss:3.01235294342041
Epoch:2, Step:3980, Discriminator Loss:0.4275888204574585, Generator Loss:2.61230731010437
Epoch:2, Step:3990, Discriminator Loss:0.5096415281295776, Generator Loss:2.0041327476501465
Epoch:2, Step:4000, Discriminator Loss:0.3804737329483032, Generator Loss:3.5895698070526123
Epoch:2, Step:4010, Discriminator Loss:0.34948500990867615, Generator Loss:4.3608856201171875
Epoch:2, Step:4020, Discriminator Loss:0.41821128129959106, Generator Loss:2.7460432052612305
Epoch:2, Step:4030, Discriminator Loss:0.4093029201030731, Generator Loss:3.807096004486084
Epoch:2, Step:4040, Discriminator Loss:0.34571945667266846, Generator Loss:4.848977088928223
Epoch:2, Step:4050, Discriminator Loss:0.3550015091896057, Generator Loss:3.865196943283081
Epoch:2, Step:4060, Discriminator Loss:0.5361716151237488, Generator Loss:2.408503293991089
Epoch:2, Step:4070, Discriminator Loss:0.4511719048023224, Generator Loss:2.6346585750579834
Epoch:2, Step:4080, Discriminator Loss:0.3963897228240967, Generator Loss:3.1348204612731934
Epoch:2, Step:4090, Discriminator Loss:0.340044230222702, Generator Loss:4.8688249588012695
Epoch:2, Step:4100, Discriminator Loss:0.37028348445892334, Generator Loss:3.7605199813842773
Epoch:2, Step:4110, Discriminator Loss:0.3758836090564728, Generator Loss:3.747002601623535
Epoch:2, Step:4120, Discriminator Loss:0.3381437361240387, Generator Loss:6.726865768432617
Epoch:2, Step:4130, Discriminator Loss:0.3558097183704376, Generator Loss:4.533326148986816
Epoch:2, Step:4140, Discriminator Loss:0.35882413387298584, Generator Loss:4.044493675231934
Epoch:2, Step:4150, Discriminator Loss:0.3337569534778595, Generator Loss:5.250494003295898
Epoch:2, Step:4160, Discriminator Loss:0.3327028155326843, Generator Loss:5.469306945800781
Epoch:2, Step:4170, Discriminator Loss:2.9264161586761475, Generator Loss:8.621785163879395
Epoch:2, Step:4180, Discriminator Loss:0.5897419452667236, Generator Loss:1.651222586631775
Epoch:2, Step:4190, Discriminator Loss:0.5795649290084839, Generator Loss:2.1339781284332275
Epoch:2, Step:4200, Discriminator Loss:0.47330933809280396, Generator Loss:2.139251708984375
Epoch:2, Step:4210, Discriminator Loss:0.3817439675331116, Generator Loss:3.4342198371887207
Epoch:2, Step:4220, Discriminator Loss:3.6495003700256348, Generator Loss:0.09946530312299728
Epoch:2, Step:4230, Discriminator Loss:0.5693274736404419, Generator Loss:2.2357728481292725
Epoch:2, Step:4240, Discriminator Loss:2.07228946685791, Generator Loss:0.34269124269485474
Epoch:2, Step:4250, Discriminator Loss:0.4548714756965637, Generator Loss:2.3779451847076416
Epoch:2, Step:4260, Discriminator Loss:0.42262572050094604, Generator Loss:2.807438373565674
Epoch:2, Step:4270, Discriminator Loss:0.4022690951824188, Generator Loss:2.9340596199035645
Epoch:2, Step:4280, Discriminator Loss:0.5839630365371704, Generator Loss:1.7220754623413086
Epoch:2, Step:4290, Discriminator Loss:4.774697780609131, Generator Loss:0.0316573791205883
Epoch:2, Step:4300, Discriminator Loss:0.861138105392456, Generator Loss:1.0673826932907104
Epoch:2, Step:4310, Discriminator Loss:0.6394418478012085, Generator Loss:1.6533337831497192
Epoch:2, Step:4320, Discriminator Loss:0.4944742023944855, Generator Loss:2.0781188011169434
Epoch:2, Step:4330, Discriminator Loss:0.4121180474758148, Generator Loss:2.7967824935913086
Epoch:2, Step:4340, Discriminator Loss:0.41764411330223083, Generator Loss:2.931208610534668
Epoch:2, Step:4350, Discriminator Loss:0.3777002990245819, Generator Loss:4.009499549865723
Epoch:2, Step:4360, Discriminator Loss:0.3520161807537079, Generator Loss:4.063794136047363
Epoch:2, Step:4370, Discriminator Loss:0.34900641441345215, Generator Loss:4.127507209777832
Epoch:2, Step:4380, Discriminator Loss:0.33141088485717773, Generator Loss:6.211503982543945
Epoch:2, Step:4390, Discriminator Loss:1.1517748832702637, Generator Loss:1.0835983753204346
Epoch:2, Step:4400, Discriminator Loss:0.3609602451324463, Generator Loss:3.953657627105713
Epoch:2, Step:4410, Discriminator Loss:0.34343209862709045, Generator Loss:4.4169487953186035
Epoch:2, Step:4420, Discriminator Loss:0.36362147331237793, Generator Loss:3.488083839416504
Epoch:2, Step:4430, Discriminator Loss:0.37325015664100647, Generator Loss:3.5089285373687744
Epoch:2, Step:4440, Discriminator Loss:0.4001968204975128, Generator Loss:2.968931198120117
Epoch:2, Step:4450, Discriminator Loss:0.3470683693885803, Generator Loss:4.44631290435791
Epoch:2, Step:4460, Discriminator Loss:0.4263422191143036, Generator Loss:3.0036919116973877
Epoch:2, Step:4470, Discriminator Loss:0.4288943111896515, Generator Loss:3.287308692932129
Epoch:2, Step:4480, Discriminator Loss:0.3336966931819916, Generator Loss:5.379048824310303
Epoch:2, Step:4490, Discriminator Loss:0.33913227915763855, Generator Loss:4.750510215759277
Epoch:2, Step:4500, Discriminator Loss:0.3323499262332916, Generator Loss:5.585028171539307
Epoch:2, Step:4510, Discriminator Loss:0.4010798931121826, Generator Loss:3.4291205406188965
Epoch:2, Step:4520, Discriminator Loss:0.33170124888420105, Generator Loss:5.710971832275391
Epoch:2, Step:4530, Discriminator Loss:0.3628586232662201, Generator Loss:4.5829315185546875
Epoch:2, Step:4540, Discriminator Loss:0.4070911109447479, Generator Loss:4.73866081237793
Epoch:2, Step:4550, Discriminator Loss:2.506978988647461, Generator Loss:7.036658763885498
Epoch:2, Step:4560, Discriminator Loss:0.9458277821540833, Generator Loss:0.9595398306846619
Epoch:2, Step:4570, Discriminator Loss:0.5121827125549316, Generator Loss:2.025123357772827
Epoch:2, Step:4580, Discriminator Loss:1.6871031522750854, Generator Loss:3.8221492767333984
Epoch:2, Step:4590, Discriminator Loss:0.7921610474586487, Generator Loss:1.2628577947616577
Epoch:2, Step:4600, Discriminator Loss:0.3851725161075592, Generator Loss:3.6799538135528564
Epoch:2, Step:4610, Discriminator Loss:0.3919067978858948, Generator Loss:3.1039185523986816
Epoch:2, Step:4620, Discriminator Loss:0.35085734724998474, Generator Loss:4.515036582946777
Epoch:2, Step:4630, Discriminator Loss:1.492239236831665, Generator Loss:0.7004040479660034
Epoch:2, Step:4640, Discriminator Loss:1.4065380096435547, Generator Loss:5.043657302856445
Epoch:2, Step:4650, Discriminator Loss:0.37886881828308105, Generator Loss:3.620715618133545
Epoch:2, Step:4660, Discriminator Loss:0.363897442817688, Generator Loss:3.886934518814087
Epoch:2, Step:4670, Discriminator Loss:0.3978898227214813, Generator Loss:3.244019031524658
Epoch:2, Step:4680, Discriminator Loss:0.3888053894042969, Generator Loss:3.0507590770721436
Epoch:2, Step:4690, Discriminator Loss:0.4595744013786316, Generator Loss:2.6863534450531006
Epoch:2, Step:4700, Discriminator Loss:0.42426609992980957, Generator Loss:2.6639628410339355
Epoch:2, Step:4710, Discriminator Loss:0.3494515120983124, Generator Loss:4.3362016677856445
Epoch:2, Step:4720, Discriminator Loss:0.5342716574668884, Generator Loss:2.6049370765686035
Epoch:2, Step:4730, Discriminator Loss:0.3851075768470764, Generator Loss:3.1999149322509766
Epoch:2, Step:4740, Discriminator Loss:0.4594268500804901, Generator Loss:2.759953022003174
Epoch:2, Step:4750, Discriminator Loss:0.3696751892566681, Generator Loss:3.338589668273926
Epoch:2, Step:4760, Discriminator Loss:0.34301117062568665, Generator Loss:4.766873836517334
Epoch:2, Step:4770, Discriminator Loss:0.34891006350517273, Generator Loss:4.375406265258789
Epoch:2, Step:4780, Discriminator Loss:0.38929229974746704, Generator Loss:4.578670501708984
Epoch:2, Step:4790, Discriminator Loss:0.3359844982624054, Generator Loss:5.481080055236816
Epoch:2, Step:4800, Discriminator Loss:0.363955557346344, Generator Loss:4.346746444702148

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [15]:
batch_size = 25
z_dim = 100
learning_rate = 0.0005
beta1 = 0.2


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch:1, Step:10, Discriminator Loss:17.243261337280273, Generator Loss:0.0002577802224550396
Epoch:1, Step:20, Discriminator Loss:2.0954394340515137, Generator Loss:2.412564754486084
Epoch:1, Step:30, Discriminator Loss:3.2301182746887207, Generator Loss:0.08042317628860474
Epoch:1, Step:40, Discriminator Loss:2.2405452728271484, Generator Loss:0.4633733034133911
Epoch:1, Step:50, Discriminator Loss:1.5959970951080322, Generator Loss:0.5305660367012024
Epoch:1, Step:60, Discriminator Loss:1.827073335647583, Generator Loss:0.3392280340194702
Epoch:1, Step:70, Discriminator Loss:1.6800973415374756, Generator Loss:1.1795518398284912
Epoch:1, Step:80, Discriminator Loss:3.218679189682007, Generator Loss:0.10568207502365112
Epoch:1, Step:90, Discriminator Loss:0.954916775226593, Generator Loss:2.4043467044830322
Epoch:1, Step:100, Discriminator Loss:2.6492390632629395, Generator Loss:0.13879072666168213
Epoch:1, Step:110, Discriminator Loss:0.7293169498443604, Generator Loss:1.3574249744415283
Epoch:1, Step:120, Discriminator Loss:0.4589689373970032, Generator Loss:3.6930646896362305
Epoch:1, Step:130, Discriminator Loss:1.0238453149795532, Generator Loss:0.9337087273597717
Epoch:1, Step:140, Discriminator Loss:0.8714245557785034, Generator Loss:1.036078929901123
Epoch:1, Step:150, Discriminator Loss:1.2821600437164307, Generator Loss:0.7861599326133728
Epoch:1, Step:160, Discriminator Loss:0.4877394735813141, Generator Loss:2.0567729473114014
Epoch:1, Step:170, Discriminator Loss:0.34380805492401123, Generator Loss:4.61354923248291
Epoch:1, Step:180, Discriminator Loss:0.9191279411315918, Generator Loss:1.0439448356628418
Epoch:1, Step:190, Discriminator Loss:0.3483765423297882, Generator Loss:4.91799259185791
Epoch:1, Step:200, Discriminator Loss:0.5321443676948547, Generator Loss:2.6058874130249023
Epoch:1, Step:210, Discriminator Loss:0.7622479796409607, Generator Loss:1.5811902284622192
Epoch:1, Step:220, Discriminator Loss:0.655357301235199, Generator Loss:1.5787503719329834
Epoch:1, Step:230, Discriminator Loss:1.7809680700302124, Generator Loss:0.6164370775222778
Epoch:1, Step:240, Discriminator Loss:1.4983222484588623, Generator Loss:0.7669079899787903
Epoch:1, Step:250, Discriminator Loss:0.999550461769104, Generator Loss:1.0390808582305908
Epoch:1, Step:260, Discriminator Loss:1.3179529905319214, Generator Loss:3.989339828491211
Epoch:1, Step:270, Discriminator Loss:0.9418840408325195, Generator Loss:0.9038022756576538
Epoch:1, Step:280, Discriminator Loss:0.7495626211166382, Generator Loss:4.061749458312988
Epoch:1, Step:290, Discriminator Loss:0.3566147983074188, Generator Loss:5.049970626831055
Epoch:1, Step:300, Discriminator Loss:0.3382607400417328, Generator Loss:6.158174991607666
Epoch:1, Step:310, Discriminator Loss:0.4363734722137451, Generator Loss:2.4531664848327637
Epoch:1, Step:320, Discriminator Loss:0.538418710231781, Generator Loss:2.16367769241333
Epoch:1, Step:330, Discriminator Loss:1.2807223796844482, Generator Loss:0.9040365815162659
Epoch:1, Step:340, Discriminator Loss:5.240042686462402, Generator Loss:4.4004621505737305
Epoch:1, Step:350, Discriminator Loss:0.514086127281189, Generator Loss:5.046624183654785
Epoch:1, Step:360, Discriminator Loss:0.3391492962837219, Generator Loss:5.311422348022461
Epoch:1, Step:370, Discriminator Loss:0.6242604851722717, Generator Loss:1.4805384874343872
Epoch:1, Step:380, Discriminator Loss:0.42883625626564026, Generator Loss:8.226548194885254
Epoch:1, Step:390, Discriminator Loss:0.688294529914856, Generator Loss:3.709805488586426
Epoch:1, Step:400, Discriminator Loss:0.3746239244937897, Generator Loss:5.292692184448242
Epoch:1, Step:410, Discriminator Loss:0.3289104700088501, Generator Loss:7.452846527099609
Epoch:1, Step:420, Discriminator Loss:0.33967623114585876, Generator Loss:5.0670166015625
Epoch:1, Step:430, Discriminator Loss:0.5968952178955078, Generator Loss:1.8444675207138062
Epoch:1, Step:440, Discriminator Loss:2.29412841796875, Generator Loss:6.183699607849121
Epoch:1, Step:450, Discriminator Loss:0.7533730268478394, Generator Loss:1.1467534303665161
Epoch:1, Step:460, Discriminator Loss:0.7528168559074402, Generator Loss:1.2146213054656982
Epoch:1, Step:470, Discriminator Loss:0.3532378673553467, Generator Loss:4.038111686706543
Epoch:1, Step:480, Discriminator Loss:2.007389545440674, Generator Loss:0.8119045495986938
Epoch:1, Step:490, Discriminator Loss:1.061031699180603, Generator Loss:0.8458185195922852
Epoch:1, Step:500, Discriminator Loss:1.363457441329956, Generator Loss:2.1175529956817627
Epoch:1, Step:510, Discriminator Loss:1.1370667219161987, Generator Loss:2.043522596359253
Epoch:1, Step:520, Discriminator Loss:0.3646920919418335, Generator Loss:4.429206848144531
Epoch:1, Step:530, Discriminator Loss:0.4163838326931, Generator Loss:2.8079535961151123
Epoch:1, Step:540, Discriminator Loss:0.340347021818161, Generator Loss:7.345691680908203
Epoch:1, Step:550, Discriminator Loss:0.4130890369415283, Generator Loss:2.8789658546447754
Epoch:1, Step:560, Discriminator Loss:2.709202289581299, Generator Loss:3.3676981925964355
Epoch:1, Step:570, Discriminator Loss:2.5478029251098633, Generator Loss:0.1616659164428711
Epoch:1, Step:580, Discriminator Loss:1.7717766761779785, Generator Loss:0.38399791717529297
Epoch:1, Step:590, Discriminator Loss:0.4743974208831787, Generator Loss:2.313396453857422
Epoch:1, Step:600, Discriminator Loss:0.35385772585868835, Generator Loss:4.07579231262207
Epoch:1, Step:610, Discriminator Loss:0.4931472837924957, Generator Loss:2.071167230606079
Epoch:1, Step:620, Discriminator Loss:0.41362160444259644, Generator Loss:3.3214550018310547
Epoch:1, Step:630, Discriminator Loss:1.407204270362854, Generator Loss:6.137818336486816
Epoch:1, Step:640, Discriminator Loss:0.4191773533821106, Generator Loss:2.9242660999298096
Epoch:1, Step:650, Discriminator Loss:0.41426342725753784, Generator Loss:5.943277359008789
Epoch:1, Step:660, Discriminator Loss:0.34231239557266235, Generator Loss:4.862704277038574
Epoch:1, Step:670, Discriminator Loss:2.6712141036987305, Generator Loss:2.93098783493042
Epoch:1, Step:680, Discriminator Loss:3.8899476528167725, Generator Loss:3.6304476261138916
Epoch:1, Step:690, Discriminator Loss:1.036840796470642, Generator Loss:0.7284027338027954
Epoch:1, Step:700, Discriminator Loss:0.49023711681365967, Generator Loss:3.488396644592285
Epoch:1, Step:710, Discriminator Loss:0.34358498454093933, Generator Loss:5.065610885620117
Epoch:1, Step:720, Discriminator Loss:0.6821941137313843, Generator Loss:1.3664236068725586
Epoch:1, Step:730, Discriminator Loss:0.3497760593891144, Generator Loss:4.667008399963379
Epoch:1, Step:740, Discriminator Loss:3.2449817657470703, Generator Loss:0.08437051624059677
Epoch:1, Step:750, Discriminator Loss:0.6447494626045227, Generator Loss:2.0740854740142822
Epoch:1, Step:760, Discriminator Loss:0.37065979838371277, Generator Loss:3.408198356628418
Epoch:1, Step:770, Discriminator Loss:0.7476041316986084, Generator Loss:1.4499777555465698
Epoch:1, Step:780, Discriminator Loss:0.7522486448287964, Generator Loss:1.1747318506240845
Epoch:1, Step:790, Discriminator Loss:0.4924483001232147, Generator Loss:2.271660804748535
Epoch:1, Step:800, Discriminator Loss:0.6855360269546509, Generator Loss:1.3478872776031494
Epoch:1, Step:810, Discriminator Loss:0.3431338667869568, Generator Loss:6.435554027557373
Epoch:1, Step:820, Discriminator Loss:0.7764445543289185, Generator Loss:1.4045138359069824
Epoch:1, Step:830, Discriminator Loss:0.4715791940689087, Generator Loss:2.597874164581299
Epoch:1, Step:840, Discriminator Loss:1.0230464935302734, Generator Loss:0.9945434927940369
Epoch:1, Step:850, Discriminator Loss:0.5691553950309753, Generator Loss:1.7508647441864014
Epoch:1, Step:860, Discriminator Loss:2.320446491241455, Generator Loss:3.076310873031616
Epoch:1, Step:870, Discriminator Loss:0.3694363236427307, Generator Loss:4.000698089599609
Epoch:1, Step:880, Discriminator Loss:1.0360513925552368, Generator Loss:1.5667493343353271
Epoch:1, Step:890, Discriminator Loss:0.42293694615364075, Generator Loss:3.3313236236572266
Epoch:1, Step:900, Discriminator Loss:0.3900509476661682, Generator Loss:3.677297830581665
Epoch:1, Step:910, Discriminator Loss:0.40826109051704407, Generator Loss:2.768343925476074
Epoch:1, Step:920, Discriminator Loss:0.38642746210098267, Generator Loss:3.5606908798217773
Epoch:1, Step:930, Discriminator Loss:0.667076826095581, Generator Loss:1.4868720769882202
Epoch:1, Step:940, Discriminator Loss:0.37438949942588806, Generator Loss:3.441638469696045
Epoch:1, Step:950, Discriminator Loss:0.6609646081924438, Generator Loss:1.4469692707061768
Epoch:1, Step:960, Discriminator Loss:1.4219319820404053, Generator Loss:3.2090342044830322
Epoch:1, Step:970, Discriminator Loss:1.4144397974014282, Generator Loss:1.1005642414093018
Epoch:1, Step:980, Discriminator Loss:1.9872289896011353, Generator Loss:0.32102271914482117
Epoch:1, Step:990, Discriminator Loss:1.5398900508880615, Generator Loss:0.4336003065109253
Epoch:1, Step:1000, Discriminator Loss:1.1929383277893066, Generator Loss:0.7161399126052856
Epoch:1, Step:1010, Discriminator Loss:1.1681592464447021, Generator Loss:0.8370649814605713
Epoch:1, Step:1020, Discriminator Loss:1.4177931547164917, Generator Loss:1.0675855875015259
Epoch:1, Step:1030, Discriminator Loss:1.130333423614502, Generator Loss:0.889820396900177
Epoch:1, Step:1040, Discriminator Loss:0.7638777494430542, Generator Loss:1.4431419372558594
Epoch:1, Step:1050, Discriminator Loss:1.2834465503692627, Generator Loss:0.741628885269165
Epoch:1, Step:1060, Discriminator Loss:0.6028410196304321, Generator Loss:1.621190071105957
Epoch:1, Step:1070, Discriminator Loss:1.2727432250976562, Generator Loss:5.126959800720215
Epoch:1, Step:1080, Discriminator Loss:0.39564234018325806, Generator Loss:4.05020809173584
Epoch:1, Step:1090, Discriminator Loss:0.5834320783615112, Generator Loss:1.642964243888855
Epoch:1, Step:1100, Discriminator Loss:0.504749059677124, Generator Loss:3.5395426750183105
Epoch:1, Step:1110, Discriminator Loss:1.4674670696258545, Generator Loss:0.823187530040741
Epoch:1, Step:1120, Discriminator Loss:0.7205694317817688, Generator Loss:4.049415588378906
Epoch:1, Step:1130, Discriminator Loss:1.08591890335083, Generator Loss:0.7356369495391846
Epoch:1, Step:1140, Discriminator Loss:0.34761252999305725, Generator Loss:5.1072001457214355
Epoch:1, Step:1150, Discriminator Loss:0.5620347857475281, Generator Loss:1.7067582607269287
Epoch:1, Step:1160, Discriminator Loss:1.1630440950393677, Generator Loss:0.8487260341644287
Epoch:1, Step:1170, Discriminator Loss:1.1229772567749023, Generator Loss:0.864465594291687
Epoch:1, Step:1180, Discriminator Loss:0.5810027122497559, Generator Loss:1.7194528579711914
Epoch:1, Step:1190, Discriminator Loss:1.7428158521652222, Generator Loss:3.1417412757873535
Epoch:1, Step:1200, Discriminator Loss:0.5570365786552429, Generator Loss:3.126710891723633
Epoch:1, Step:1210, Discriminator Loss:1.326886534690857, Generator Loss:0.5213523507118225
Epoch:1, Step:1220, Discriminator Loss:1.1945104598999023, Generator Loss:0.6932339072227478
Epoch:1, Step:1230, Discriminator Loss:1.0175433158874512, Generator Loss:1.1228396892547607
Epoch:1, Step:1240, Discriminator Loss:1.601918339729309, Generator Loss:4.123772144317627
Epoch:1, Step:1250, Discriminator Loss:2.025031328201294, Generator Loss:0.44699639081954956
Epoch:1, Step:1260, Discriminator Loss:1.3353713750839233, Generator Loss:0.9618028402328491
Epoch:1, Step:1270, Discriminator Loss:1.2646536827087402, Generator Loss:0.5402498245239258
Epoch:1, Step:1280, Discriminator Loss:1.391918659210205, Generator Loss:1.1629056930541992
Epoch:1, Step:1290, Discriminator Loss:1.6001182794570923, Generator Loss:3.5517449378967285
Epoch:1, Step:1300, Discriminator Loss:1.8212764263153076, Generator Loss:0.27737855911254883
Epoch:1, Step:1310, Discriminator Loss:1.6210699081420898, Generator Loss:0.6785989999771118
Epoch:1, Step:1320, Discriminator Loss:1.523430585861206, Generator Loss:0.8172996044158936
Epoch:1, Step:1330, Discriminator Loss:1.2410681247711182, Generator Loss:0.8339030742645264
Epoch:1, Step:1340, Discriminator Loss:1.0155354738235474, Generator Loss:0.7703713774681091
Epoch:1, Step:1350, Discriminator Loss:1.0325418710708618, Generator Loss:0.7995556592941284
Epoch:1, Step:1360, Discriminator Loss:1.3998650312423706, Generator Loss:1.618896722793579
Epoch:1, Step:1370, Discriminator Loss:0.9091050624847412, Generator Loss:1.4129154682159424
Epoch:1, Step:1380, Discriminator Loss:1.6891096830368042, Generator Loss:2.5402438640594482
Epoch:1, Step:1390, Discriminator Loss:0.7362517714500427, Generator Loss:1.4130139350891113
Epoch:1, Step:1400, Discriminator Loss:0.6229466199874878, Generator Loss:1.5196400880813599
Epoch:1, Step:1410, Discriminator Loss:0.5834392309188843, Generator Loss:1.7130029201507568
Epoch:1, Step:1420, Discriminator Loss:2.735964059829712, Generator Loss:0.10469632595777512
Epoch:1, Step:1430, Discriminator Loss:1.3115500211715698, Generator Loss:0.7172296643257141
Epoch:1, Step:1440, Discriminator Loss:1.2844109535217285, Generator Loss:0.755445122718811
Epoch:1, Step:1450, Discriminator Loss:1.4388432502746582, Generator Loss:0.8120794892311096
Epoch:1, Step:1460, Discriminator Loss:1.0549603700637817, Generator Loss:0.8299940228462219
Epoch:1, Step:1470, Discriminator Loss:1.0353906154632568, Generator Loss:1.7139286994934082
Epoch:1, Step:1480, Discriminator Loss:0.8348001837730408, Generator Loss:1.1913907527923584
Epoch:1, Step:1490, Discriminator Loss:1.616027593612671, Generator Loss:0.8531167507171631
Epoch:1, Step:1500, Discriminator Loss:1.4139751195907593, Generator Loss:0.758864164352417
Epoch:1, Step:1510, Discriminator Loss:1.4652034044265747, Generator Loss:0.7395286560058594
Epoch:1, Step:1520, Discriminator Loss:1.895058512687683, Generator Loss:1.5670909881591797
Epoch:1, Step:1530, Discriminator Loss:1.5411570072174072, Generator Loss:0.5822234153747559
Epoch:1, Step:1540, Discriminator Loss:1.2996933460235596, Generator Loss:0.5906730890274048
Epoch:1, Step:1550, Discriminator Loss:1.2078359127044678, Generator Loss:1.590754508972168
Epoch:1, Step:1560, Discriminator Loss:1.1303904056549072, Generator Loss:0.6980125904083252
Epoch:1, Step:1570, Discriminator Loss:0.6534830331802368, Generator Loss:1.5526669025421143
Epoch:1, Step:1580, Discriminator Loss:1.5627155303955078, Generator Loss:1.2369804382324219
Epoch:1, Step:1590, Discriminator Loss:1.4677577018737793, Generator Loss:0.8217751383781433
Epoch:1, Step:1600, Discriminator Loss:1.3317276239395142, Generator Loss:0.5128981471061707
Epoch:1, Step:1610, Discriminator Loss:1.322850227355957, Generator Loss:0.5772850513458252
Epoch:1, Step:1620, Discriminator Loss:0.5595635175704956, Generator Loss:1.8082985877990723
Epoch:1, Step:1630, Discriminator Loss:1.4586158990859985, Generator Loss:0.5501831769943237
Epoch:1, Step:1640, Discriminator Loss:1.3286242485046387, Generator Loss:0.7410386204719543
Epoch:1, Step:1650, Discriminator Loss:1.3972877264022827, Generator Loss:0.5871869921684265
Epoch:1, Step:1660, Discriminator Loss:1.3262168169021606, Generator Loss:1.0677673816680908
Epoch:1, Step:1670, Discriminator Loss:1.414160132408142, Generator Loss:0.8004539012908936
Epoch:1, Step:1680, Discriminator Loss:1.629696249961853, Generator Loss:0.38318493962287903
Epoch:1, Step:1690, Discriminator Loss:1.3044548034667969, Generator Loss:0.7118948698043823
Epoch:1, Step:1700, Discriminator Loss:1.23876953125, Generator Loss:0.7804110050201416
Epoch:1, Step:1710, Discriminator Loss:2.121328115463257, Generator Loss:0.22333985567092896
Epoch:1, Step:1720, Discriminator Loss:1.177735447883606, Generator Loss:0.7409855723381042
Epoch:1, Step:1730, Discriminator Loss:1.3419511318206787, Generator Loss:0.6586952805519104
Epoch:1, Step:1740, Discriminator Loss:1.8808695077896118, Generator Loss:1.6490226984024048
Epoch:1, Step:1750, Discriminator Loss:1.6508331298828125, Generator Loss:0.34189051389694214
Epoch:1, Step:1760, Discriminator Loss:1.632676362991333, Generator Loss:0.3666117191314697
Epoch:1, Step:1770, Discriminator Loss:1.3548117876052856, Generator Loss:1.4215110540390015
Epoch:1, Step:1780, Discriminator Loss:1.4196645021438599, Generator Loss:0.7980607748031616
Epoch:1, Step:1790, Discriminator Loss:1.4029399156570435, Generator Loss:0.7748912572860718
Epoch:1, Step:1800, Discriminator Loss:1.6845886707305908, Generator Loss:0.34307336807250977
Epoch:1, Step:1810, Discriminator Loss:1.7649586200714111, Generator Loss:0.3165104389190674
Epoch:1, Step:1820, Discriminator Loss:1.508249044418335, Generator Loss:0.7213743925094604
Epoch:1, Step:1830, Discriminator Loss:1.2911125421524048, Generator Loss:0.6785051822662354
Epoch:1, Step:1840, Discriminator Loss:1.2528300285339355, Generator Loss:2.081583023071289
Epoch:1, Step:1850, Discriminator Loss:1.6038093566894531, Generator Loss:0.44570788741111755
Epoch:1, Step:1860, Discriminator Loss:1.7990705966949463, Generator Loss:2.2703893184661865
Epoch:1, Step:1870, Discriminator Loss:2.2460999488830566, Generator Loss:0.21076524257659912
Epoch:1, Step:1880, Discriminator Loss:1.9343361854553223, Generator Loss:0.3539426922798157
Epoch:1, Step:1890, Discriminator Loss:1.0958499908447266, Generator Loss:1.6229121685028076
Epoch:1, Step:1900, Discriminator Loss:1.0182154178619385, Generator Loss:0.9052181243896484
Epoch:1, Step:1910, Discriminator Loss:1.2746230363845825, Generator Loss:1.027052879333496
Epoch:1, Step:1920, Discriminator Loss:1.2406537532806396, Generator Loss:1.4504631757736206
Epoch:1, Step:1930, Discriminator Loss:2.4339206218719482, Generator Loss:2.5454087257385254
Epoch:1, Step:1940, Discriminator Loss:0.710233211517334, Generator Loss:1.8152096271514893
Epoch:1, Step:1950, Discriminator Loss:1.2170088291168213, Generator Loss:0.908787727355957
Epoch:1, Step:1960, Discriminator Loss:0.9355126619338989, Generator Loss:1.2387648820877075
Epoch:1, Step:1970, Discriminator Loss:0.9675815105438232, Generator Loss:0.8372543454170227
Epoch:1, Step:1980, Discriminator Loss:0.87342369556427, Generator Loss:1.0124588012695312
Epoch:1, Step:1990, Discriminator Loss:1.8906137943267822, Generator Loss:0.8763570785522461
Epoch:1, Step:2000, Discriminator Loss:2.019148826599121, Generator Loss:0.2506195902824402
Epoch:1, Step:2010, Discriminator Loss:1.5875418186187744, Generator Loss:0.41751739382743835
Epoch:1, Step:2020, Discriminator Loss:1.473610281944275, Generator Loss:0.9812039136886597
Epoch:1, Step:2030, Discriminator Loss:1.3999844789505005, Generator Loss:0.9037287831306458
Epoch:1, Step:2040, Discriminator Loss:1.4646563529968262, Generator Loss:0.8258144855499268
Epoch:1, Step:2050, Discriminator Loss:1.330594778060913, Generator Loss:0.9258420467376709
Epoch:1, Step:2060, Discriminator Loss:1.3742060661315918, Generator Loss:1.3341090679168701
Epoch:1, Step:2070, Discriminator Loss:1.3724955320358276, Generator Loss:0.5926171541213989
Epoch:1, Step:2080, Discriminator Loss:2.538811206817627, Generator Loss:2.8085286617279053
Epoch:1, Step:2090, Discriminator Loss:1.545269250869751, Generator Loss:0.37309136986732483
Epoch:1, Step:2100, Discriminator Loss:1.4262471199035645, Generator Loss:0.5090412497520447
Epoch:1, Step:2110, Discriminator Loss:2.09452223777771, Generator Loss:0.21663159132003784
Epoch:1, Step:2120, Discriminator Loss:1.6531319618225098, Generator Loss:0.5583846569061279
Epoch:1, Step:2130, Discriminator Loss:1.3891167640686035, Generator Loss:0.7095859050750732
Epoch:1, Step:2140, Discriminator Loss:1.1652393341064453, Generator Loss:0.758356511592865
Epoch:1, Step:2150, Discriminator Loss:1.074263572692871, Generator Loss:0.7793788909912109
Epoch:1, Step:2160, Discriminator Loss:1.437112808227539, Generator Loss:0.8855059146881104
Epoch:1, Step:2170, Discriminator Loss:1.1999845504760742, Generator Loss:0.8917477130889893
Epoch:1, Step:2180, Discriminator Loss:1.3173818588256836, Generator Loss:0.6272609233856201
Epoch:1, Step:2190, Discriminator Loss:2.3245198726654053, Generator Loss:0.1711767017841339
Epoch:1, Step:2200, Discriminator Loss:2.0106420516967773, Generator Loss:0.22537261247634888
Epoch:1, Step:2210, Discriminator Loss:1.314283847808838, Generator Loss:2.0337471961975098
Epoch:1, Step:2220, Discriminator Loss:1.7216451168060303, Generator Loss:3.0273618698120117
Epoch:1, Step:2230, Discriminator Loss:0.8481408357620239, Generator Loss:0.977664589881897
Epoch:1, Step:2240, Discriminator Loss:1.3889362812042236, Generator Loss:3.4760971069335938
Epoch:1, Step:2250, Discriminator Loss:1.2264732122421265, Generator Loss:0.6545679569244385
Epoch:1, Step:2260, Discriminator Loss:1.2534949779510498, Generator Loss:2.9228053092956543
Epoch:1, Step:2270, Discriminator Loss:0.8494222164154053, Generator Loss:0.9997524619102478
Epoch:1, Step:2280, Discriminator Loss:1.5717931985855103, Generator Loss:1.3002917766571045
Epoch:1, Step:2290, Discriminator Loss:1.3860249519348145, Generator Loss:1.0458301305770874
Epoch:1, Step:2300, Discriminator Loss:0.6866230368614197, Generator Loss:1.737056016921997
Epoch:1, Step:2310, Discriminator Loss:0.4737914502620697, Generator Loss:2.0953047275543213
Epoch:1, Step:2320, Discriminator Loss:0.5668145418167114, Generator Loss:2.0468077659606934
Epoch:1, Step:2330, Discriminator Loss:0.5723217725753784, Generator Loss:1.5983827114105225
Epoch:1, Step:2340, Discriminator Loss:0.975853443145752, Generator Loss:1.3147406578063965
Epoch:1, Step:2350, Discriminator Loss:0.3895707428455353, Generator Loss:3.074533462524414
Epoch:1, Step:2360, Discriminator Loss:1.4285688400268555, Generator Loss:0.5097538232803345
Epoch:1, Step:2370, Discriminator Loss:0.462823748588562, Generator Loss:3.1872169971466064
Epoch:1, Step:2380, Discriminator Loss:0.6041125655174255, Generator Loss:3.7417514324188232
Epoch:1, Step:2390, Discriminator Loss:1.7064317464828491, Generator Loss:2.1761953830718994
Epoch:1, Step:2400, Discriminator Loss:0.6819166541099548, Generator Loss:1.9126603603363037
Epoch:1, Step:2410, Discriminator Loss:0.47947850823402405, Generator Loss:2.540522336959839
Epoch:1, Step:2420, Discriminator Loss:0.8677738308906555, Generator Loss:0.9689862728118896
Epoch:1, Step:2430, Discriminator Loss:0.45343196392059326, Generator Loss:3.1362833976745605
Epoch:1, Step:2440, Discriminator Loss:0.636620283126831, Generator Loss:1.5143789052963257
Epoch:1, Step:2450, Discriminator Loss:0.5222810506820679, Generator Loss:1.8629964590072632
Epoch:1, Step:2460, Discriminator Loss:0.366984486579895, Generator Loss:3.5578510761260986
Epoch:1, Step:2470, Discriminator Loss:0.3718385100364685, Generator Loss:3.4712116718292236
Epoch:1, Step:2480, Discriminator Loss:0.36094796657562256, Generator Loss:3.8219006061553955
Epoch:1, Step:2490, Discriminator Loss:0.4772111475467682, Generator Loss:2.2100582122802734
Epoch:1, Step:2500, Discriminator Loss:0.7951668500900269, Generator Loss:1.1536966562271118
Epoch:1, Step:2510, Discriminator Loss:0.519808292388916, Generator Loss:2.0188660621643066
Epoch:1, Step:2520, Discriminator Loss:0.6050899028778076, Generator Loss:1.6552940607070923
Epoch:1, Step:2530, Discriminator Loss:0.5831702947616577, Generator Loss:1.6895536184310913
Epoch:1, Step:2540, Discriminator Loss:0.6127074360847473, Generator Loss:1.5593748092651367
Epoch:1, Step:2550, Discriminator Loss:0.503373920917511, Generator Loss:1.9709758758544922
Epoch:1, Step:2560, Discriminator Loss:1.4254417419433594, Generator Loss:0.5128891468048096
Epoch:1, Step:2570, Discriminator Loss:0.7434136867523193, Generator Loss:1.2460602521896362
Epoch:1, Step:2580, Discriminator Loss:0.6446676850318909, Generator Loss:3.600825309753418
Epoch:1, Step:2590, Discriminator Loss:0.3640816807746887, Generator Loss:3.997117280960083
Epoch:1, Step:2600, Discriminator Loss:0.7801425457000732, Generator Loss:1.2450147867202759
Epoch:1, Step:2610, Discriminator Loss:1.097755789756775, Generator Loss:0.9868975877761841
Epoch:1, Step:2620, Discriminator Loss:1.2872734069824219, Generator Loss:0.7018042206764221
Epoch:1, Step:2630, Discriminator Loss:2.1995177268981934, Generator Loss:0.2262742817401886
Epoch:1, Step:2640, Discriminator Loss:1.0560227632522583, Generator Loss:0.7897704839706421
Epoch:1, Step:2650, Discriminator Loss:0.601787805557251, Generator Loss:1.8327434062957764
Epoch:1, Step:2660, Discriminator Loss:0.4860621988773346, Generator Loss:2.321467161178589
Epoch:1, Step:2670, Discriminator Loss:1.0132997035980225, Generator Loss:0.7841351628303528
Epoch:1, Step:2680, Discriminator Loss:0.47644317150115967, Generator Loss:2.700162172317505
Epoch:1, Step:2690, Discriminator Loss:0.4582292139530182, Generator Loss:2.3210086822509766
Epoch:1, Step:2700, Discriminator Loss:0.49692657589912415, Generator Loss:2.097569465637207
Epoch:1, Step:2710, Discriminator Loss:0.5230798721313477, Generator Loss:1.9751402139663696
Epoch:1, Step:2720, Discriminator Loss:0.37069129943847656, Generator Loss:3.5971031188964844
Epoch:1, Step:2730, Discriminator Loss:0.3619590103626251, Generator Loss:3.7097089290618896
Epoch:1, Step:2740, Discriminator Loss:2.6212291717529297, Generator Loss:3.383204936981201
Epoch:1, Step:2750, Discriminator Loss:1.934424638748169, Generator Loss:0.261552631855011
Epoch:1, Step:2760, Discriminator Loss:0.8329277038574219, Generator Loss:1.5706102848052979
Epoch:1, Step:2770, Discriminator Loss:1.5371047258377075, Generator Loss:0.43730485439300537
Epoch:1, Step:2780, Discriminator Loss:1.3694608211517334, Generator Loss:0.996911883354187
Epoch:1, Step:2790, Discriminator Loss:1.4606801271438599, Generator Loss:0.5070938467979431
Epoch:1, Step:2800, Discriminator Loss:1.2216739654541016, Generator Loss:0.7113265991210938
Epoch:1, Step:2810, Discriminator Loss:1.3590915203094482, Generator Loss:0.8845676183700562
Epoch:1, Step:2820, Discriminator Loss:1.431121826171875, Generator Loss:0.6315667033195496
Epoch:1, Step:2830, Discriminator Loss:1.4296865463256836, Generator Loss:0.9063321352005005
Epoch:1, Step:2840, Discriminator Loss:1.1854205131530762, Generator Loss:1.1089526414871216
Epoch:1, Step:2850, Discriminator Loss:1.3040359020233154, Generator Loss:1.131425142288208
Epoch:1, Step:2860, Discriminator Loss:1.1239418983459473, Generator Loss:0.7961252331733704
Epoch:1, Step:2870, Discriminator Loss:1.489124059677124, Generator Loss:0.5771338939666748
Epoch:1, Step:2880, Discriminator Loss:1.2902958393096924, Generator Loss:1.0900421142578125
Epoch:1, Step:2890, Discriminator Loss:1.882648229598999, Generator Loss:2.5256476402282715
Epoch:1, Step:2900, Discriminator Loss:1.1909141540527344, Generator Loss:1.4676423072814941
Epoch:1, Step:2910, Discriminator Loss:1.3926262855529785, Generator Loss:0.5030673146247864
Epoch:1, Step:2920, Discriminator Loss:1.127094030380249, Generator Loss:0.8337510824203491
Epoch:1, Step:2930, Discriminator Loss:1.1768473386764526, Generator Loss:0.8633990287780762
Epoch:1, Step:2940, Discriminator Loss:1.1402029991149902, Generator Loss:0.7828292846679688
Epoch:1, Step:2950, Discriminator Loss:1.34083890914917, Generator Loss:1.282928705215454
Epoch:1, Step:2960, Discriminator Loss:1.6692633628845215, Generator Loss:0.35463035106658936
Epoch:1, Step:2970, Discriminator Loss:1.162256121635437, Generator Loss:0.6665942668914795
Epoch:1, Step:2980, Discriminator Loss:2.5396485328674316, Generator Loss:2.53174090385437
Epoch:1, Step:2990, Discriminator Loss:1.2193043231964111, Generator Loss:1.0252528190612793
Epoch:1, Step:3000, Discriminator Loss:1.1614190340042114, Generator Loss:0.8072418570518494
Epoch:1, Step:3010, Discriminator Loss:1.806274652481079, Generator Loss:0.39090657234191895
Epoch:1, Step:3020, Discriminator Loss:1.024679183959961, Generator Loss:1.2281062602996826
Epoch:1, Step:3030, Discriminator Loss:0.9432023167610168, Generator Loss:1.979783535003662
Epoch:1, Step:3040, Discriminator Loss:1.2671294212341309, Generator Loss:0.7080920338630676
Epoch:1, Step:3050, Discriminator Loss:1.2100000381469727, Generator Loss:0.6350603699684143
Epoch:1, Step:3060, Discriminator Loss:1.2782336473464966, Generator Loss:0.7439724206924438
Epoch:1, Step:3070, Discriminator Loss:1.3497755527496338, Generator Loss:0.677166759967804
Epoch:1, Step:3080, Discriminator Loss:1.3599895238876343, Generator Loss:0.5414137244224548
Epoch:1, Step:3090, Discriminator Loss:1.9536035060882568, Generator Loss:0.23818494379520416
Epoch:1, Step:3100, Discriminator Loss:1.2653030157089233, Generator Loss:0.6156533360481262
Epoch:1, Step:3110, Discriminator Loss:1.5997865200042725, Generator Loss:0.43474701046943665
Epoch:1, Step:3120, Discriminator Loss:1.0594652891159058, Generator Loss:1.0165343284606934
Epoch:1, Step:3130, Discriminator Loss:1.3694441318511963, Generator Loss:0.734725832939148
Epoch:1, Step:3140, Discriminator Loss:1.4152781963348389, Generator Loss:0.5522249937057495
Epoch:1, Step:3150, Discriminator Loss:1.3273429870605469, Generator Loss:1.0432069301605225
Epoch:1, Step:3160, Discriminator Loss:1.257350206375122, Generator Loss:0.6755201816558838
Epoch:1, Step:3170, Discriminator Loss:1.2094895839691162, Generator Loss:0.6161736249923706
Epoch:1, Step:3180, Discriminator Loss:1.0485694408416748, Generator Loss:0.8966847658157349
Epoch:1, Step:3190, Discriminator Loss:0.9817432165145874, Generator Loss:1.0759351253509521
Epoch:1, Step:3200, Discriminator Loss:1.800469994544983, Generator Loss:0.3407433032989502
Epoch:1, Step:3210, Discriminator Loss:0.9395014047622681, Generator Loss:1.1293647289276123
Epoch:1, Step:3220, Discriminator Loss:1.3768905401229858, Generator Loss:3.160374164581299
Epoch:1, Step:3230, Discriminator Loss:1.7759692668914795, Generator Loss:0.3021760880947113
Epoch:1, Step:3240, Discriminator Loss:1.4308719635009766, Generator Loss:0.6433759331703186
Epoch:1, Step:3250, Discriminator Loss:0.8660498261451721, Generator Loss:2.2735490798950195
Epoch:1, Step:3260, Discriminator Loss:0.613525927066803, Generator Loss:1.9049720764160156
Epoch:1, Step:3270, Discriminator Loss:1.1293439865112305, Generator Loss:1.030855655670166
Epoch:1, Step:3280, Discriminator Loss:1.3252995014190674, Generator Loss:0.5169333219528198
Epoch:1, Step:3290, Discriminator Loss:1.4938435554504395, Generator Loss:0.44206684827804565
Epoch:1, Step:3300, Discriminator Loss:0.42451852560043335, Generator Loss:2.703007221221924
Epoch:1, Step:3310, Discriminator Loss:1.7724190950393677, Generator Loss:0.30995869636535645
Epoch:1, Step:3320, Discriminator Loss:1.3100839853286743, Generator Loss:0.5318784713745117
Epoch:1, Step:3330, Discriminator Loss:1.441328763961792, Generator Loss:1.163149118423462
Epoch:1, Step:3340, Discriminator Loss:1.2836445569992065, Generator Loss:1.7704306840896606
Epoch:1, Step:3350, Discriminator Loss:2.6577091217041016, Generator Loss:0.14219354093074799
Epoch:1, Step:3360, Discriminator Loss:1.4758410453796387, Generator Loss:0.765113353729248
Epoch:1, Step:3370, Discriminator Loss:1.5431007146835327, Generator Loss:0.48661357164382935
Epoch:1, Step:3380, Discriminator Loss:1.2028748989105225, Generator Loss:0.6240252256393433
Epoch:1, Step:3390, Discriminator Loss:1.2188369035720825, Generator Loss:0.8729147911071777
Epoch:1, Step:3400, Discriminator Loss:1.2407288551330566, Generator Loss:0.6475670337677002
Epoch:1, Step:3410, Discriminator Loss:1.3393845558166504, Generator Loss:1.9361727237701416
Epoch:1, Step:3420, Discriminator Loss:1.5928738117218018, Generator Loss:0.4324498474597931
Epoch:1, Step:3430, Discriminator Loss:1.1772817373275757, Generator Loss:1.3222377300262451
Epoch:1, Step:3440, Discriminator Loss:0.9104786515235901, Generator Loss:1.042953372001648
Epoch:1, Step:3450, Discriminator Loss:0.858487606048584, Generator Loss:1.0468008518218994
Epoch:1, Step:3460, Discriminator Loss:0.7741446495056152, Generator Loss:1.1381163597106934
Epoch:1, Step:3470, Discriminator Loss:1.0863940715789795, Generator Loss:1.5989396572113037
Epoch:1, Step:3480, Discriminator Loss:1.8888425827026367, Generator Loss:0.2997564971446991
Epoch:1, Step:3490, Discriminator Loss:0.7064474821090698, Generator Loss:1.3730942010879517
Epoch:1, Step:3500, Discriminator Loss:1.6435743570327759, Generator Loss:2.54514741897583
Epoch:1, Step:3510, Discriminator Loss:0.7878516316413879, Generator Loss:1.3237380981445312
Epoch:1, Step:3520, Discriminator Loss:1.2751213312149048, Generator Loss:0.5478537082672119
Epoch:1, Step:3530, Discriminator Loss:1.2670501470565796, Generator Loss:0.5607863664627075
Epoch:1, Step:3540, Discriminator Loss:0.7524900436401367, Generator Loss:3.4293503761291504
Epoch:1, Step:3550, Discriminator Loss:0.7318015694618225, Generator Loss:2.2607603073120117
Epoch:1, Step:3560, Discriminator Loss:0.579188346862793, Generator Loss:1.651872158050537
Epoch:1, Step:3570, Discriminator Loss:0.5037730932235718, Generator Loss:2.0632781982421875
Epoch:1, Step:3580, Discriminator Loss:2.342792510986328, Generator Loss:3.248095989227295
Epoch:1, Step:3590, Discriminator Loss:1.9313982725143433, Generator Loss:0.3029212951660156
Epoch:1, Step:3600, Discriminator Loss:0.8282085061073303, Generator Loss:1.0135494470596313
Epoch:1, Step:3610, Discriminator Loss:1.659476399421692, Generator Loss:3.9151875972747803
Epoch:1, Step:3620, Discriminator Loss:0.6500300765037537, Generator Loss:1.5672602653503418
Epoch:1, Step:3630, Discriminator Loss:0.5517773628234863, Generator Loss:2.001739501953125
Epoch:1, Step:3640, Discriminator Loss:0.7693002223968506, Generator Loss:6.450171947479248
Epoch:1, Step:3650, Discriminator Loss:0.9168683886528015, Generator Loss:1.2158589363098145
Epoch:1, Step:3660, Discriminator Loss:1.26338791847229, Generator Loss:0.5472058057785034
Epoch:1, Step:3670, Discriminator Loss:0.538162350654602, Generator Loss:2.0552964210510254
Epoch:1, Step:3680, Discriminator Loss:0.9283785820007324, Generator Loss:1.0555260181427002
Epoch:1, Step:3690, Discriminator Loss:0.3567998707294464, Generator Loss:4.284548282623291
Epoch:1, Step:3700, Discriminator Loss:0.3971211314201355, Generator Loss:2.9574379920959473
Epoch:1, Step:3710, Discriminator Loss:0.6175678968429565, Generator Loss:4.81547737121582
Epoch:1, Step:3720, Discriminator Loss:0.5625856518745422, Generator Loss:2.691065788269043
Epoch:1, Step:3730, Discriminator Loss:1.6238547563552856, Generator Loss:0.36741429567337036
Epoch:1, Step:3740, Discriminator Loss:0.6669559478759766, Generator Loss:1.3698890209197998
Epoch:1, Step:3750, Discriminator Loss:0.3761734962463379, Generator Loss:3.3084897994995117
Epoch:1, Step:3760, Discriminator Loss:0.6096154451370239, Generator Loss:1.7258886098861694
Epoch:1, Step:3770, Discriminator Loss:0.5114033222198486, Generator Loss:2.010039806365967
Epoch:1, Step:3780, Discriminator Loss:1.3528342247009277, Generator Loss:0.568100094795227
Epoch:1, Step:3790, Discriminator Loss:1.573839783668518, Generator Loss:0.45811784267425537
Epoch:1, Step:3800, Discriminator Loss:0.5764490962028503, Generator Loss:2.2933273315429688
Epoch:1, Step:3810, Discriminator Loss:0.6368319988250732, Generator Loss:1.453667402267456
Epoch:1, Step:3820, Discriminator Loss:0.48995599150657654, Generator Loss:3.7040488719940186
Epoch:1, Step:3830, Discriminator Loss:0.41365358233451843, Generator Loss:2.9246103763580322
Epoch:1, Step:3840, Discriminator Loss:2.381977081298828, Generator Loss:0.18038445711135864
Epoch:1, Step:3850, Discriminator Loss:1.131290078163147, Generator Loss:1.705749273300171
Epoch:1, Step:3860, Discriminator Loss:1.4172840118408203, Generator Loss:0.4662238359451294
Epoch:1, Step:3870, Discriminator Loss:0.8452239036560059, Generator Loss:1.0194940567016602
Epoch:1, Step:3880, Discriminator Loss:0.9638198614120483, Generator Loss:0.9333691596984863
Epoch:1, Step:3890, Discriminator Loss:0.49556800723075867, Generator Loss:2.125239849090576
Epoch:1, Step:3900, Discriminator Loss:1.3360815048217773, Generator Loss:0.5233489871025085
Epoch:1, Step:3910, Discriminator Loss:0.6783773303031921, Generator Loss:1.3158904314041138
Epoch:1, Step:3920, Discriminator Loss:1.4178845882415771, Generator Loss:0.6620674133300781
Epoch:1, Step:3930, Discriminator Loss:1.4847387075424194, Generator Loss:0.44179391860961914
Epoch:1, Step:3940, Discriminator Loss:1.0235912799835205, Generator Loss:3.4242453575134277
Epoch:1, Step:3950, Discriminator Loss:0.395878404378891, Generator Loss:3.1247339248657227
Epoch:1, Step:3960, Discriminator Loss:0.7934347987174988, Generator Loss:1.136454463005066
Epoch:1, Step:3970, Discriminator Loss:1.3139015436172485, Generator Loss:0.6437907218933105
Epoch:1, Step:3980, Discriminator Loss:1.4701403379440308, Generator Loss:0.5947302579879761
Epoch:1, Step:3990, Discriminator Loss:1.1299996376037598, Generator Loss:0.8752479553222656
Epoch:1, Step:4000, Discriminator Loss:2.3877973556518555, Generator Loss:0.1710384637117386
Epoch:1, Step:4010, Discriminator Loss:0.7322379350662231, Generator Loss:1.957759141921997
Epoch:1, Step:4020, Discriminator Loss:2.0584657192230225, Generator Loss:2.877697467803955
Epoch:1, Step:4030, Discriminator Loss:1.4646943807601929, Generator Loss:3.7423033714294434
Epoch:1, Step:4040, Discriminator Loss:0.8451029062271118, Generator Loss:1.0710909366607666
Epoch:1, Step:4050, Discriminator Loss:0.4850461483001709, Generator Loss:2.3364815711975098
Epoch:1, Step:4060, Discriminator Loss:1.051789402961731, Generator Loss:2.4435648918151855
Epoch:1, Step:4070, Discriminator Loss:0.5708874464035034, Generator Loss:2.43328857421875
Epoch:1, Step:4080, Discriminator Loss:0.9830530881881714, Generator Loss:2.93916916847229
Epoch:1, Step:4090, Discriminator Loss:1.1628458499908447, Generator Loss:0.6256707906723022
Epoch:1, Step:4100, Discriminator Loss:0.4310643672943115, Generator Loss:4.616520881652832
Epoch:1, Step:4110, Discriminator Loss:0.5232248306274414, Generator Loss:3.2210066318511963
Epoch:1, Step:4120, Discriminator Loss:1.407336950302124, Generator Loss:0.5271726846694946
Epoch:1, Step:4130, Discriminator Loss:0.7395761609077454, Generator Loss:1.3950613737106323
Epoch:1, Step:4140, Discriminator Loss:2.6720335483551025, Generator Loss:2.931372880935669
Epoch:1, Step:4150, Discriminator Loss:0.472574383020401, Generator Loss:2.842261791229248
Epoch:1, Step:4160, Discriminator Loss:0.42649513483047485, Generator Loss:2.68947696685791
Epoch:1, Step:4170, Discriminator Loss:0.6023287177085876, Generator Loss:1.6987591981887817
Epoch:1, Step:4180, Discriminator Loss:0.561089277267456, Generator Loss:1.714205265045166
Epoch:1, Step:4190, Discriminator Loss:0.690088152885437, Generator Loss:1.4205002784729004
Epoch:1, Step:4200, Discriminator Loss:0.7051873207092285, Generator Loss:1.4309661388397217
Epoch:1, Step:4210, Discriminator Loss:0.5939232110977173, Generator Loss:2.5796756744384766
Epoch:1, Step:4220, Discriminator Loss:0.47300201654434204, Generator Loss:2.2836532592773438
Epoch:1, Step:4230, Discriminator Loss:1.2248477935791016, Generator Loss:1.3887348175048828
Epoch:1, Step:4240, Discriminator Loss:0.8225038051605225, Generator Loss:1.2116672992706299
Epoch:1, Step:4250, Discriminator Loss:0.7676580548286438, Generator Loss:3.473939895629883
Epoch:1, Step:4260, Discriminator Loss:1.9441484212875366, Generator Loss:0.25972703099250793
Epoch:1, Step:4270, Discriminator Loss:0.6809106469154358, Generator Loss:2.758272171020508
Epoch:1, Step:4280, Discriminator Loss:0.7643394470214844, Generator Loss:2.4536643028259277
Epoch:1, Step:4290, Discriminator Loss:0.4972350001335144, Generator Loss:3.423788547515869
Epoch:1, Step:4300, Discriminator Loss:0.6479727625846863, Generator Loss:1.570873498916626
Epoch:1, Step:4310, Discriminator Loss:0.8686927556991577, Generator Loss:1.0106639862060547
Epoch:1, Step:4320, Discriminator Loss:0.5879306197166443, Generator Loss:3.1069302558898926
Epoch:1, Step:4330, Discriminator Loss:0.6161928176879883, Generator Loss:3.7755558490753174
Epoch:1, Step:4340, Discriminator Loss:1.4152500629425049, Generator Loss:0.46022963523864746
Epoch:1, Step:4350, Discriminator Loss:0.4874550700187683, Generator Loss:2.093655586242676
Epoch:1, Step:4360, Discriminator Loss:0.4465566873550415, Generator Loss:2.5065054893493652
Epoch:1, Step:4370, Discriminator Loss:0.8201258182525635, Generator Loss:1.1018075942993164
Epoch:1, Step:4380, Discriminator Loss:0.5037596225738525, Generator Loss:2.1134796142578125
Epoch:1, Step:4390, Discriminator Loss:1.6138859987258911, Generator Loss:1.4800875186920166
Epoch:1, Step:4400, Discriminator Loss:0.8345171213150024, Generator Loss:1.028893232345581
Epoch:1, Step:4410, Discriminator Loss:0.8765097260475159, Generator Loss:1.0701370239257812
Epoch:1, Step:4420, Discriminator Loss:0.37821826338768005, Generator Loss:4.368061065673828
Epoch:1, Step:4430, Discriminator Loss:1.069318175315857, Generator Loss:0.7361259460449219
Epoch:1, Step:4440, Discriminator Loss:2.2954928874969482, Generator Loss:0.17738716304302216
Epoch:1, Step:4450, Discriminator Loss:1.0455677509307861, Generator Loss:1.1983164548873901
Epoch:1, Step:4460, Discriminator Loss:1.784544825553894, Generator Loss:1.5614490509033203
Epoch:1, Step:4470, Discriminator Loss:1.032805323600769, Generator Loss:1.292079210281372
Epoch:1, Step:4480, Discriminator Loss:0.8000079393386841, Generator Loss:1.1811275482177734
Epoch:1, Step:4490, Discriminator Loss:0.4014544188976288, Generator Loss:3.1594173908233643
Epoch:1, Step:4500, Discriminator Loss:1.1070430278778076, Generator Loss:0.703038215637207
Epoch:1, Step:4510, Discriminator Loss:1.320939302444458, Generator Loss:0.5478218793869019
Epoch:1, Step:4520, Discriminator Loss:1.350278615951538, Generator Loss:0.7723044157028198
Epoch:1, Step:4530, Discriminator Loss:1.259223222732544, Generator Loss:1.175628662109375
Epoch:1, Step:4540, Discriminator Loss:0.6489654183387756, Generator Loss:2.455199718475342
Epoch:1, Step:4550, Discriminator Loss:0.8421103954315186, Generator Loss:0.970431923866272
Epoch:1, Step:4560, Discriminator Loss:1.37734055519104, Generator Loss:0.4845848083496094
Epoch:1, Step:4570, Discriminator Loss:0.9471115469932556, Generator Loss:0.8886554837226868
Epoch:1, Step:4580, Discriminator Loss:1.7138487100601196, Generator Loss:0.3494079113006592
Epoch:1, Step:4590, Discriminator Loss:1.2389159202575684, Generator Loss:0.5808243155479431
Epoch:1, Step:4600, Discriminator Loss:0.5561270713806152, Generator Loss:3.0973925590515137
Epoch:1, Step:4610, Discriminator Loss:0.7021512985229492, Generator Loss:2.118079423904419
Epoch:1, Step:4620, Discriminator Loss:1.190153956413269, Generator Loss:0.6239494681358337
Epoch:1, Step:4630, Discriminator Loss:0.7871957421302795, Generator Loss:1.3364181518554688
Epoch:1, Step:4640, Discriminator Loss:0.8584956526756287, Generator Loss:1.031051516532898
Epoch:1, Step:4650, Discriminator Loss:1.1872478723526, Generator Loss:0.6505419611930847
Epoch:1, Step:4660, Discriminator Loss:0.701081395149231, Generator Loss:1.3015904426574707
Epoch:1, Step:4670, Discriminator Loss:0.43147188425064087, Generator Loss:3.393965721130371
Epoch:1, Step:4680, Discriminator Loss:1.301174521446228, Generator Loss:1.8562915325164795
Epoch:1, Step:4690, Discriminator Loss:1.092832326889038, Generator Loss:0.7356210350990295
Epoch:1, Step:4700, Discriminator Loss:0.836155891418457, Generator Loss:1.4411275386810303
Epoch:1, Step:4710, Discriminator Loss:3.1459319591522217, Generator Loss:2.963365316390991
Epoch:1, Step:4720, Discriminator Loss:0.753697395324707, Generator Loss:1.1997177600860596
Epoch:1, Step:4730, Discriminator Loss:0.45177993178367615, Generator Loss:2.6932835578918457
Epoch:1, Step:4740, Discriminator Loss:0.6166670322418213, Generator Loss:1.6718177795410156
Epoch:1, Step:4750, Discriminator Loss:0.6886682510375977, Generator Loss:1.646834373474121
Epoch:1, Step:4760, Discriminator Loss:0.5883796811103821, Generator Loss:2.5174741744995117
Epoch:1, Step:4770, Discriminator Loss:0.5590614676475525, Generator Loss:2.8461649417877197
Epoch:1, Step:4780, Discriminator Loss:1.0255383253097534, Generator Loss:3.6173367500305176
Epoch:1, Step:4790, Discriminator Loss:1.8752713203430176, Generator Loss:0.3442770838737488
Epoch:1, Step:4800, Discriminator Loss:1.4896628856658936, Generator Loss:0.4523128867149353
Epoch:1, Step:4810, Discriminator Loss:2.2357895374298096, Generator Loss:0.1864732801914215
Epoch:1, Step:4820, Discriminator Loss:0.9089197516441345, Generator Loss:0.9163452982902527
Epoch:1, Step:4830, Discriminator Loss:2.620551109313965, Generator Loss:0.12920834124088287
Epoch:1, Step:4840, Discriminator Loss:1.062695860862732, Generator Loss:0.7558552026748657
Epoch:1, Step:4850, Discriminator Loss:0.7526962757110596, Generator Loss:1.1974401473999023
Epoch:1, Step:4860, Discriminator Loss:1.0513114929199219, Generator Loss:1.3470022678375244
Epoch:1, Step:4870, Discriminator Loss:0.979440450668335, Generator Loss:0.8150142431259155
Epoch:1, Step:4880, Discriminator Loss:1.2422109842300415, Generator Loss:0.6524333953857422
Epoch:1, Step:4890, Discriminator Loss:1.4991874694824219, Generator Loss:2.1584959030151367
Epoch:1, Step:4900, Discriminator Loss:1.3172403573989868, Generator Loss:0.6191814541816711
Epoch:1, Step:4910, Discriminator Loss:1.1265983581542969, Generator Loss:0.7130982875823975
Epoch:1, Step:4920, Discriminator Loss:2.1150031089782715, Generator Loss:0.20442509651184082
Epoch:1, Step:4930, Discriminator Loss:0.999048113822937, Generator Loss:1.018477439880371
Epoch:1, Step:4940, Discriminator Loss:1.4586700201034546, Generator Loss:0.4384157657623291
Epoch:1, Step:4950, Discriminator Loss:0.9471620321273804, Generator Loss:1.0958185195922852
Epoch:1, Step:4960, Discriminator Loss:1.2319378852844238, Generator Loss:0.642701268196106
Epoch:1, Step:4970, Discriminator Loss:1.2816342115402222, Generator Loss:0.7036114931106567
Epoch:1, Step:4980, Discriminator Loss:1.978419542312622, Generator Loss:0.2361225187778473
Epoch:1, Step:4990, Discriminator Loss:1.5189745426177979, Generator Loss:0.5213872194290161
Epoch:1, Step:5000, Discriminator Loss:1.2590354681015015, Generator Loss:0.5698482990264893
Epoch:1, Step:5010, Discriminator Loss:1.3955633640289307, Generator Loss:0.497587114572525
Epoch:1, Step:5020, Discriminator Loss:1.6467406749725342, Generator Loss:0.3458194136619568
Epoch:1, Step:5030, Discriminator Loss:1.5569641590118408, Generator Loss:0.39622724056243896
Epoch:1, Step:5040, Discriminator Loss:1.2162542343139648, Generator Loss:1.1588199138641357
Epoch:1, Step:5050, Discriminator Loss:1.416170358657837, Generator Loss:0.5380831956863403
Epoch:1, Step:5060, Discriminator Loss:1.3239330053329468, Generator Loss:1.8135168552398682
Epoch:1, Step:5070, Discriminator Loss:1.6572266817092896, Generator Loss:0.35382217168807983
Epoch:1, Step:5080, Discriminator Loss:1.4409210681915283, Generator Loss:0.5308294892311096
Epoch:1, Step:5090, Discriminator Loss:1.1653203964233398, Generator Loss:0.8934073448181152
Epoch:1, Step:5100, Discriminator Loss:1.184872031211853, Generator Loss:0.7228676080703735
Epoch:1, Step:5110, Discriminator Loss:1.0922510623931885, Generator Loss:0.9914052486419678
Epoch:1, Step:5120, Discriminator Loss:1.470214605331421, Generator Loss:0.4163840115070343
Epoch:1, Step:5130, Discriminator Loss:1.150346040725708, Generator Loss:0.6968674659729004
Epoch:1, Step:5140, Discriminator Loss:1.1781121492385864, Generator Loss:1.3814988136291504
Epoch:1, Step:5150, Discriminator Loss:1.0736825466156006, Generator Loss:1.437251091003418
Epoch:1, Step:5160, Discriminator Loss:1.0961742401123047, Generator Loss:0.796238362789154
Epoch:1, Step:5170, Discriminator Loss:1.7985278367996216, Generator Loss:0.30559420585632324
Epoch:1, Step:5180, Discriminator Loss:2.4405620098114014, Generator Loss:0.14933699369430542
Epoch:1, Step:5190, Discriminator Loss:1.4809411764144897, Generator Loss:0.43173354864120483
Epoch:1, Step:5200, Discriminator Loss:1.5050445795059204, Generator Loss:0.4969238340854645
Epoch:1, Step:5210, Discriminator Loss:0.8741679191589355, Generator Loss:1.5989391803741455
Epoch:1, Step:5220, Discriminator Loss:1.2514636516571045, Generator Loss:1.1254968643188477
Epoch:1, Step:5230, Discriminator Loss:1.4836857318878174, Generator Loss:0.43295082449913025
Epoch:1, Step:5240, Discriminator Loss:1.7156858444213867, Generator Loss:0.3350047469139099
Epoch:1, Step:5250, Discriminator Loss:1.3389230966567993, Generator Loss:0.5117682814598083
Epoch:1, Step:5260, Discriminator Loss:0.9317535161972046, Generator Loss:0.9734084010124207
Epoch:1, Step:5270, Discriminator Loss:1.0215659141540527, Generator Loss:0.9363570809364319
Epoch:1, Step:5280, Discriminator Loss:1.0493547916412354, Generator Loss:0.7512425780296326
Epoch:1, Step:5290, Discriminator Loss:1.0187087059020996, Generator Loss:0.7930818796157837
Epoch:1, Step:5300, Discriminator Loss:1.1882073879241943, Generator Loss:0.621204137802124
Epoch:1, Step:5310, Discriminator Loss:1.2285858392715454, Generator Loss:1.3985291719436646
Epoch:1, Step:5320, Discriminator Loss:0.8721219897270203, Generator Loss:1.0384963750839233
Epoch:1, Step:5330, Discriminator Loss:1.2200353145599365, Generator Loss:0.7520116567611694
Epoch:1, Step:5340, Discriminator Loss:0.6533696055412292, Generator Loss:1.7026243209838867
Epoch:1, Step:5350, Discriminator Loss:1.628247618675232, Generator Loss:0.36016780138015747
Epoch:1, Step:5360, Discriminator Loss:1.103389024734497, Generator Loss:0.7203952074050903
Epoch:1, Step:5370, Discriminator Loss:0.9770199656486511, Generator Loss:1.0267599821090698
Epoch:1, Step:5380, Discriminator Loss:0.9826829433441162, Generator Loss:0.9364789128303528
Epoch:1, Step:5390, Discriminator Loss:1.7063188552856445, Generator Loss:0.3651021718978882
Epoch:1, Step:5400, Discriminator Loss:1.125673532485962, Generator Loss:0.8578322529792786
Epoch:1, Step:5410, Discriminator Loss:1.276167392730713, Generator Loss:0.8273749351501465
Epoch:1, Step:5420, Discriminator Loss:0.7150837182998657, Generator Loss:1.5233219861984253
Epoch:1, Step:5430, Discriminator Loss:2.142936944961548, Generator Loss:0.20995613932609558
Epoch:1, Step:5440, Discriminator Loss:1.0740524530410767, Generator Loss:0.9555000066757202
Epoch:1, Step:5450, Discriminator Loss:1.4098703861236572, Generator Loss:1.8345677852630615
Epoch:1, Step:5460, Discriminator Loss:1.1754206418991089, Generator Loss:0.9725106954574585
Epoch:1, Step:5470, Discriminator Loss:0.7573318481445312, Generator Loss:1.1866025924682617
Epoch:1, Step:5480, Discriminator Loss:1.0511925220489502, Generator Loss:0.8397347331047058
Epoch:1, Step:5490, Discriminator Loss:1.8123517036437988, Generator Loss:0.3361791968345642
Epoch:1, Step:5500, Discriminator Loss:1.3781766891479492, Generator Loss:0.8413856029510498
Epoch:1, Step:5510, Discriminator Loss:1.324742317199707, Generator Loss:0.7683844566345215
Epoch:1, Step:5520, Discriminator Loss:2.041588068008423, Generator Loss:0.2253008782863617
Epoch:1, Step:5530, Discriminator Loss:0.9825443029403687, Generator Loss:0.9993848204612732
Epoch:1, Step:5540, Discriminator Loss:0.91008061170578, Generator Loss:1.7048014402389526
Epoch:1, Step:5550, Discriminator Loss:0.8890178799629211, Generator Loss:1.4401085376739502
Epoch:1, Step:5560, Discriminator Loss:1.5609554052352905, Generator Loss:2.1433143615722656
Epoch:1, Step:5570, Discriminator Loss:0.8146206140518188, Generator Loss:1.0835132598876953
Epoch:1, Step:5580, Discriminator Loss:2.178650140762329, Generator Loss:0.23671412467956543
Epoch:1, Step:5590, Discriminator Loss:1.2103272676467896, Generator Loss:0.6117523312568665
Epoch:1, Step:5600, Discriminator Loss:0.6113680005073547, Generator Loss:1.9885478019714355
Epoch:1, Step:5610, Discriminator Loss:1.0394070148468018, Generator Loss:2.7187747955322266
Epoch:1, Step:5620, Discriminator Loss:1.107100248336792, Generator Loss:1.3350410461425781
Epoch:1, Step:5630, Discriminator Loss:1.008070707321167, Generator Loss:0.9792828559875488
Epoch:1, Step:5640, Discriminator Loss:1.1145858764648438, Generator Loss:0.8995572328567505
Epoch:1, Step:5650, Discriminator Loss:1.2520424127578735, Generator Loss:0.6076971292495728
Epoch:1, Step:5660, Discriminator Loss:0.8888418674468994, Generator Loss:1.4924064874649048
Epoch:1, Step:5670, Discriminator Loss:1.0495039224624634, Generator Loss:0.9283847808837891
Epoch:1, Step:5680, Discriminator Loss:1.2698338031768799, Generator Loss:0.5732969045639038
Epoch:1, Step:5690, Discriminator Loss:1.3805079460144043, Generator Loss:0.5497983694076538
Epoch:1, Step:5700, Discriminator Loss:1.309256911277771, Generator Loss:0.7759652137756348
Epoch:1, Step:5710, Discriminator Loss:1.585766315460205, Generator Loss:0.376537948846817
Epoch:1, Step:5720, Discriminator Loss:0.944908857345581, Generator Loss:1.4252362251281738
Epoch:1, Step:5730, Discriminator Loss:1.3519127368927002, Generator Loss:1.258388876914978
Epoch:1, Step:5740, Discriminator Loss:1.298790454864502, Generator Loss:2.130976676940918
Epoch:1, Step:5750, Discriminator Loss:0.9142392873764038, Generator Loss:0.9707794785499573
Epoch:1, Step:5760, Discriminator Loss:1.4226306676864624, Generator Loss:0.5027005672454834
Epoch:1, Step:5770, Discriminator Loss:1.0855103731155396, Generator Loss:1.0019521713256836
Epoch:1, Step:5780, Discriminator Loss:0.881721556186676, Generator Loss:0.9683445692062378
Epoch:1, Step:5790, Discriminator Loss:1.2122910022735596, Generator Loss:0.7165675163269043
Epoch:1, Step:5800, Discriminator Loss:1.283860445022583, Generator Loss:0.5837183594703674
Epoch:1, Step:5810, Discriminator Loss:0.7346818447113037, Generator Loss:1.716463327407837
Epoch:1, Step:5820, Discriminator Loss:0.7970993518829346, Generator Loss:1.0662202835083008
Epoch:1, Step:5830, Discriminator Loss:2.5993828773498535, Generator Loss:0.12965446710586548
Epoch:1, Step:5840, Discriminator Loss:0.5607672929763794, Generator Loss:1.8972643613815308
Epoch:1, Step:5850, Discriminator Loss:0.6178826093673706, Generator Loss:1.5085268020629883
Epoch:1, Step:5860, Discriminator Loss:1.6799967288970947, Generator Loss:0.3871624171733856
Epoch:1, Step:5870, Discriminator Loss:1.7210062742233276, Generator Loss:2.652863025665283
Epoch:1, Step:5880, Discriminator Loss:1.4577184915542603, Generator Loss:0.43899136781692505
Epoch:1, Step:5890, Discriminator Loss:1.6251881122589111, Generator Loss:0.4060802459716797
Epoch:1, Step:5900, Discriminator Loss:1.6986075639724731, Generator Loss:0.37818998098373413
Epoch:1, Step:5910, Discriminator Loss:0.4954150915145874, Generator Loss:2.2056281566619873
Epoch:1, Step:5920, Discriminator Loss:0.8890688419342041, Generator Loss:1.0743077993392944
Epoch:1, Step:5930, Discriminator Loss:0.9348435401916504, Generator Loss:1.317247748374939
Epoch:1, Step:5940, Discriminator Loss:1.4907894134521484, Generator Loss:0.4383677840232849
Epoch:1, Step:5950, Discriminator Loss:1.7883955240249634, Generator Loss:0.4622526466846466
Epoch:1, Step:5960, Discriminator Loss:0.5760378837585449, Generator Loss:1.7190985679626465
Epoch:1, Step:5970, Discriminator Loss:1.5011320114135742, Generator Loss:0.42953816056251526
Epoch:1, Step:5980, Discriminator Loss:1.7029328346252441, Generator Loss:0.3393724262714386
Epoch:1, Step:5990, Discriminator Loss:1.1025152206420898, Generator Loss:1.6504485607147217
Epoch:1, Step:6000, Discriminator Loss:0.862310528755188, Generator Loss:1.1844711303710938
Epoch:1, Step:6010, Discriminator Loss:0.9708366394042969, Generator Loss:0.8410650491714478
Epoch:1, Step:6020, Discriminator Loss:0.929557740688324, Generator Loss:1.6289002895355225
Epoch:1, Step:6030, Discriminator Loss:3.275909900665283, Generator Loss:0.06356517225503922
Epoch:1, Step:6040, Discriminator Loss:1.1308542490005493, Generator Loss:0.8641238808631897
Epoch:1, Step:6050, Discriminator Loss:1.4559781551361084, Generator Loss:0.4365171194076538
Epoch:1, Step:6060, Discriminator Loss:1.7549651861190796, Generator Loss:0.3871666491031647
Epoch:1, Step:6070, Discriminator Loss:1.6641974449157715, Generator Loss:0.44423359632492065
Epoch:1, Step:6080, Discriminator Loss:1.238523244857788, Generator Loss:1.0648417472839355
Epoch:1, Step:6090, Discriminator Loss:1.6558334827423096, Generator Loss:1.56217622756958
Epoch:1, Step:6100, Discriminator Loss:0.9016729593276978, Generator Loss:0.8766533732414246
Epoch:1, Step:6110, Discriminator Loss:0.9076545834541321, Generator Loss:1.06156587600708
Epoch:1, Step:6120, Discriminator Loss:1.3915555477142334, Generator Loss:0.5375239849090576
Epoch:1, Step:6130, Discriminator Loss:0.5693588852882385, Generator Loss:1.8097529411315918
Epoch:1, Step:6140, Discriminator Loss:0.9995430707931519, Generator Loss:1.5812222957611084
Epoch:1, Step:6150, Discriminator Loss:1.3392740488052368, Generator Loss:0.5612935423851013
Epoch:1, Step:6160, Discriminator Loss:0.5638231635093689, Generator Loss:3.2814276218414307
Epoch:1, Step:6170, Discriminator Loss:1.2241783142089844, Generator Loss:0.6425977945327759
Epoch:1, Step:6180, Discriminator Loss:0.5804612040519714, Generator Loss:3.755479335784912
Epoch:1, Step:6190, Discriminator Loss:0.878636360168457, Generator Loss:1.8816742897033691
Epoch:1, Step:6200, Discriminator Loss:0.6230254173278809, Generator Loss:2.0938878059387207
Epoch:1, Step:6210, Discriminator Loss:0.3888621926307678, Generator Loss:4.228782653808594
Epoch:1, Step:6220, Discriminator Loss:1.6331026554107666, Generator Loss:1.5049324035644531
Epoch:1, Step:6230, Discriminator Loss:1.0196304321289062, Generator Loss:1.6496965885162354
Epoch:1, Step:6240, Discriminator Loss:0.7447769641876221, Generator Loss:1.2196820974349976
Epoch:1, Step:6250, Discriminator Loss:0.9399293661117554, Generator Loss:2.148747682571411
Epoch:1, Step:6260, Discriminator Loss:2.414867877960205, Generator Loss:0.17619946599006653
Epoch:1, Step:6270, Discriminator Loss:0.6524404883384705, Generator Loss:1.4646403789520264
Epoch:1, Step:6280, Discriminator Loss:1.8815524578094482, Generator Loss:1.629852056503296
Epoch:1, Step:6290, Discriminator Loss:1.43222975730896, Generator Loss:1.1335781812667847
Epoch:1, Step:6300, Discriminator Loss:1.1953173875808716, Generator Loss:0.7360388040542603
Epoch:1, Step:6310, Discriminator Loss:1.0361602306365967, Generator Loss:1.099686622619629
Epoch:1, Step:6320, Discriminator Loss:0.9806921482086182, Generator Loss:1.0779272317886353
Epoch:1, Step:6330, Discriminator Loss:1.3602700233459473, Generator Loss:1.527868390083313
Epoch:1, Step:6340, Discriminator Loss:1.300575613975525, Generator Loss:0.5975425839424133
Epoch:1, Step:6350, Discriminator Loss:1.3807308673858643, Generator Loss:0.49117588996887207
Epoch:1, Step:6360, Discriminator Loss:1.5426037311553955, Generator Loss:0.42776086926460266
Epoch:1, Step:6370, Discriminator Loss:2.140532970428467, Generator Loss:0.20133700966835022
Epoch:1, Step:6380, Discriminator Loss:1.0848654508590698, Generator Loss:1.2973462343215942
Epoch:1, Step:6390, Discriminator Loss:1.0874598026275635, Generator Loss:0.703749418258667
Epoch:1, Step:6400, Discriminator Loss:1.8044029474258423, Generator Loss:0.2930687665939331
Epoch:1, Step:6410, Discriminator Loss:0.9968379735946655, Generator Loss:0.9871152639389038
Epoch:1, Step:6420, Discriminator Loss:1.4147965908050537, Generator Loss:0.5258333683013916
Epoch:1, Step:6430, Discriminator Loss:1.0011311769485474, Generator Loss:1.56984281539917
Epoch:1, Step:6440, Discriminator Loss:1.5403116941452026, Generator Loss:0.4363517761230469
Epoch:1, Step:6450, Discriminator Loss:1.1645622253417969, Generator Loss:2.3725342750549316
Epoch:1, Step:6460, Discriminator Loss:0.8555680513381958, Generator Loss:1.03101646900177
Epoch:1, Step:6470, Discriminator Loss:1.3624262809753418, Generator Loss:0.5906904339790344
Epoch:1, Step:6480, Discriminator Loss:0.9234371185302734, Generator Loss:0.9556959867477417
Epoch:1, Step:6490, Discriminator Loss:2.6457037925720215, Generator Loss:0.13607409596443176
Epoch:1, Step:6500, Discriminator Loss:1.3445963859558105, Generator Loss:0.4978157877922058
Epoch:1, Step:6510, Discriminator Loss:0.5485882759094238, Generator Loss:1.8568874597549438
Epoch:1, Step:6520, Discriminator Loss:1.1737524271011353, Generator Loss:0.7846180200576782
Epoch:1, Step:6530, Discriminator Loss:1.120802402496338, Generator Loss:0.9200230240821838
Epoch:1, Step:6540, Discriminator Loss:1.0481510162353516, Generator Loss:0.9272319078445435
Epoch:1, Step:6550, Discriminator Loss:0.7697042226791382, Generator Loss:1.2269010543823242
Epoch:1, Step:6560, Discriminator Loss:1.282515525817871, Generator Loss:1.2445292472839355
Epoch:1, Step:6570, Discriminator Loss:1.4218947887420654, Generator Loss:0.4959121644496918
Epoch:1, Step:6580, Discriminator Loss:1.271815538406372, Generator Loss:0.8580154776573181
Epoch:1, Step:6590, Discriminator Loss:1.3200948238372803, Generator Loss:0.586275577545166
Epoch:1, Step:6600, Discriminator Loss:1.2903108596801758, Generator Loss:1.4984822273254395
Epoch:1, Step:6610, Discriminator Loss:0.7791144251823425, Generator Loss:1.2324211597442627
Epoch:1, Step:6620, Discriminator Loss:1.2450087070465088, Generator Loss:0.7146962881088257
Epoch:1, Step:6630, Discriminator Loss:1.3132438659667969, Generator Loss:2.128048896789551
Epoch:1, Step:6640, Discriminator Loss:1.1187666654586792, Generator Loss:1.0581308603286743
Epoch:1, Step:6650, Discriminator Loss:1.3003942966461182, Generator Loss:0.6131997108459473
Epoch:1, Step:6660, Discriminator Loss:1.4176130294799805, Generator Loss:0.4958186745643616
Epoch:1, Step:6670, Discriminator Loss:1.3812259435653687, Generator Loss:0.4726330041885376
Epoch:1, Step:6680, Discriminator Loss:1.578772783279419, Generator Loss:0.4412163197994232
Epoch:1, Step:6690, Discriminator Loss:1.1740204095840454, Generator Loss:0.7190219163894653
Epoch:1, Step:6700, Discriminator Loss:1.3013741970062256, Generator Loss:0.6464473605155945
Epoch:1, Step:6710, Discriminator Loss:1.889732003211975, Generator Loss:0.2925429344177246
Epoch:1, Step:6720, Discriminator Loss:0.964266300201416, Generator Loss:1.2253578901290894
Epoch:1, Step:6730, Discriminator Loss:0.9769995808601379, Generator Loss:0.7841919660568237
Epoch:1, Step:6740, Discriminator Loss:1.5368537902832031, Generator Loss:0.44961249828338623
Epoch:1, Step:6750, Discriminator Loss:1.0620688199996948, Generator Loss:1.0668758153915405
Epoch:1, Step:6760, Discriminator Loss:2.052786111831665, Generator Loss:0.2303905189037323
Epoch:1, Step:6770, Discriminator Loss:1.4095392227172852, Generator Loss:0.49898582696914673
Epoch:1, Step:6780, Discriminator Loss:0.9015542268753052, Generator Loss:1.1047801971435547
Epoch:1, Step:6790, Discriminator Loss:2.7520592212677, Generator Loss:0.13097761571407318
Epoch:1, Step:6800, Discriminator Loss:0.5755600929260254, Generator Loss:1.7960288524627686
Epoch:1, Step:6810, Discriminator Loss:1.6683385372161865, Generator Loss:0.3371773660182953
Epoch:1, Step:6820, Discriminator Loss:1.5775483846664429, Generator Loss:0.38114258646965027
Epoch:1, Step:6830, Discriminator Loss:0.8572821617126465, Generator Loss:1.5903034210205078
Epoch:1, Step:6840, Discriminator Loss:1.0255600214004517, Generator Loss:0.754442572593689
Epoch:1, Step:6850, Discriminator Loss:0.476956307888031, Generator Loss:2.3680386543273926
Epoch:1, Step:6860, Discriminator Loss:0.649476945400238, Generator Loss:1.746311902999878
Epoch:1, Step:6870, Discriminator Loss:0.8907932043075562, Generator Loss:0.9766188263893127
Epoch:1, Step:6880, Discriminator Loss:1.486223816871643, Generator Loss:0.4298337697982788
Epoch:1, Step:6890, Discriminator Loss:1.2044432163238525, Generator Loss:0.8055440783500671
Epoch:1, Step:6900, Discriminator Loss:1.0329656600952148, Generator Loss:0.7853578925132751
Epoch:1, Step:6910, Discriminator Loss:1.6172124147415161, Generator Loss:0.4342268109321594
Epoch:1, Step:6920, Discriminator Loss:1.2282142639160156, Generator Loss:0.8682453036308289
Epoch:1, Step:6930, Discriminator Loss:1.74112868309021, Generator Loss:0.326108455657959
Epoch:1, Step:6940, Discriminator Loss:1.182145357131958, Generator Loss:0.7792317867279053
Epoch:1, Step:6950, Discriminator Loss:1.4568742513656616, Generator Loss:0.5145403146743774
Epoch:1, Step:6960, Discriminator Loss:0.7184591293334961, Generator Loss:2.526076555252075
Epoch:1, Step:6970, Discriminator Loss:0.9228716492652893, Generator Loss:1.012742280960083
Epoch:1, Step:6980, Discriminator Loss:1.1963322162628174, Generator Loss:0.9289933443069458
Epoch:1, Step:6990, Discriminator Loss:1.8478996753692627, Generator Loss:0.3136335611343384
Epoch:1, Step:7000, Discriminator Loss:1.5982375144958496, Generator Loss:0.4713593125343323
Epoch:1, Step:7010, Discriminator Loss:1.2639042139053345, Generator Loss:0.5613422393798828
Epoch:1, Step:7020, Discriminator Loss:1.4796116352081299, Generator Loss:0.5755298137664795
Epoch:1, Step:7030, Discriminator Loss:1.8904039859771729, Generator Loss:0.28420543670654297
Epoch:1, Step:7040, Discriminator Loss:1.333274006843567, Generator Loss:0.4935096502304077
Epoch:1, Step:7050, Discriminator Loss:0.6186102628707886, Generator Loss:1.650322437286377
Epoch:1, Step:7060, Discriminator Loss:1.8870365619659424, Generator Loss:0.47998863458633423
Epoch:1, Step:7070, Discriminator Loss:1.0765067338943481, Generator Loss:0.7813611030578613
Epoch:1, Step:7080, Discriminator Loss:0.8793461918830872, Generator Loss:0.9871093034744263
Epoch:1, Step:7090, Discriminator Loss:0.6651140451431274, Generator Loss:1.4731148481369019
Epoch:1, Step:7100, Discriminator Loss:1.3991700410842896, Generator Loss:1.8851451873779297
Epoch:1, Step:7110, Discriminator Loss:0.6284047365188599, Generator Loss:1.8421452045440674
Epoch:1, Step:7120, Discriminator Loss:0.4350332021713257, Generator Loss:3.196178913116455
Epoch:1, Step:7130, Discriminator Loss:0.3889402747154236, Generator Loss:3.263334274291992
Epoch:1, Step:7140, Discriminator Loss:0.4580259323120117, Generator Loss:2.415811777114868
Epoch:1, Step:7150, Discriminator Loss:0.8736066222190857, Generator Loss:1.158290147781372
Epoch:1, Step:7160, Discriminator Loss:0.9440592527389526, Generator Loss:1.2327070236206055
Epoch:1, Step:7170, Discriminator Loss:1.6868730783462524, Generator Loss:0.36613017320632935
Epoch:1, Step:7180, Discriminator Loss:0.9090874195098877, Generator Loss:0.9561808705329895
Epoch:1, Step:7190, Discriminator Loss:0.37686049938201904, Generator Loss:3.842013120651245
Epoch:1, Step:7200, Discriminator Loss:1.1175642013549805, Generator Loss:0.963310956954956
Epoch:1, Step:7210, Discriminator Loss:1.299599528312683, Generator Loss:2.1716959476470947
Epoch:1, Step:7220, Discriminator Loss:1.6016473770141602, Generator Loss:0.38729357719421387
Epoch:1, Step:7230, Discriminator Loss:1.4733915328979492, Generator Loss:0.45035940408706665
Epoch:1, Step:7240, Discriminator Loss:1.5330971479415894, Generator Loss:0.6367830634117126
Epoch:1, Step:7250, Discriminator Loss:1.690003752708435, Generator Loss:0.32915765047073364
Epoch:1, Step:7260, Discriminator Loss:1.0885944366455078, Generator Loss:0.8540025949478149
Epoch:1, Step:7270, Discriminator Loss:1.2120065689086914, Generator Loss:1.5884578227996826
Epoch:1, Step:7280, Discriminator Loss:1.536493182182312, Generator Loss:0.39828044176101685
Epoch:1, Step:7290, Discriminator Loss:1.0129729509353638, Generator Loss:0.9162261486053467
Epoch:1, Step:7300, Discriminator Loss:0.8460767269134521, Generator Loss:1.292811393737793
Epoch:1, Step:7310, Discriminator Loss:1.30574369430542, Generator Loss:0.5661969184875488
Epoch:1, Step:7320, Discriminator Loss:1.1064174175262451, Generator Loss:0.8364198207855225
Epoch:1, Step:7330, Discriminator Loss:1.1373608112335205, Generator Loss:1.129638910293579
Epoch:1, Step:7340, Discriminator Loss:1.2397129535675049, Generator Loss:0.599481999874115
Epoch:1, Step:7350, Discriminator Loss:1.3833796977996826, Generator Loss:0.6185861229896545
Epoch:1, Step:7360, Discriminator Loss:1.4410369396209717, Generator Loss:0.43946635723114014
Epoch:1, Step:7370, Discriminator Loss:1.4001063108444214, Generator Loss:0.5690828561782837
Epoch:1, Step:7380, Discriminator Loss:1.4530540704727173, Generator Loss:0.5087862014770508
Epoch:1, Step:7390, Discriminator Loss:0.971808135509491, Generator Loss:1.360253095626831
Epoch:1, Step:7400, Discriminator Loss:1.152285099029541, Generator Loss:1.2713193893432617
Epoch:1, Step:7410, Discriminator Loss:1.2115156650543213, Generator Loss:1.1275447607040405
Epoch:1, Step:7420, Discriminator Loss:1.3508732318878174, Generator Loss:0.5360206365585327
Epoch:1, Step:7430, Discriminator Loss:1.6731152534484863, Generator Loss:0.3415144085884094
Epoch:1, Step:7440, Discriminator Loss:1.2356550693511963, Generator Loss:0.6004524230957031
Epoch:1, Step:7450, Discriminator Loss:1.0656894445419312, Generator Loss:0.8126808404922485
Epoch:1, Step:7460, Discriminator Loss:1.2511591911315918, Generator Loss:0.6371116042137146
Epoch:1, Step:7470, Discriminator Loss:1.2376441955566406, Generator Loss:0.6216976642608643
Epoch:1, Step:7480, Discriminator Loss:1.697429895401001, Generator Loss:0.351793497800827
Epoch:1, Step:7490, Discriminator Loss:1.224430799484253, Generator Loss:0.8875405788421631
Epoch:1, Step:7500, Discriminator Loss:1.829723834991455, Generator Loss:0.2923203706741333
Epoch:1, Step:7510, Discriminator Loss:1.3007254600524902, Generator Loss:0.8306918144226074
Epoch:1, Step:7520, Discriminator Loss:1.108954906463623, Generator Loss:0.9024770259857178
Epoch:1, Step:7530, Discriminator Loss:1.0180219411849976, Generator Loss:1.0668153762817383
Epoch:1, Step:7540, Discriminator Loss:1.326476812362671, Generator Loss:0.5060932636260986
Epoch:1, Step:7550, Discriminator Loss:1.3565624952316284, Generator Loss:1.2283211946487427
Epoch:1, Step:7560, Discriminator Loss:1.3608624935150146, Generator Loss:0.5537821650505066
Epoch:1, Step:7570, Discriminator Loss:1.5618538856506348, Generator Loss:0.37978488206863403
Epoch:1, Step:7580, Discriminator Loss:1.6244895458221436, Generator Loss:0.36769038438796997
Epoch:1, Step:7590, Discriminator Loss:1.821969747543335, Generator Loss:0.29383569955825806
Epoch:1, Step:7600, Discriminator Loss:1.4449180364608765, Generator Loss:0.46653735637664795
Epoch:1, Step:7610, Discriminator Loss:1.2293471097946167, Generator Loss:1.904185175895691
Epoch:1, Step:7620, Discriminator Loss:0.5258921980857849, Generator Loss:2.6819167137145996
Epoch:1, Step:7630, Discriminator Loss:1.0526509284973145, Generator Loss:1.3532410860061646
Epoch:1, Step:7640, Discriminator Loss:0.8317379355430603, Generator Loss:2.411628007888794
Epoch:1, Step:7650, Discriminator Loss:0.7464468479156494, Generator Loss:1.253865361213684
Epoch:1, Step:7660, Discriminator Loss:1.1455838680267334, Generator Loss:0.681371808052063
Epoch:1, Step:7670, Discriminator Loss:1.0275828838348389, Generator Loss:0.8676254749298096
Epoch:1, Step:7680, Discriminator Loss:0.9373703598976135, Generator Loss:1.2338111400604248
Epoch:1, Step:7690, Discriminator Loss:0.5227152705192566, Generator Loss:2.592684507369995
Epoch:1, Step:7700, Discriminator Loss:1.7225180864334106, Generator Loss:2.991152286529541
Epoch:1, Step:7710, Discriminator Loss:1.1135475635528564, Generator Loss:0.7539198398590088
Epoch:1, Step:7720, Discriminator Loss:1.2828524112701416, Generator Loss:0.5246303677558899
Epoch:1, Step:7730, Discriminator Loss:0.8999351263046265, Generator Loss:0.9507514238357544
Epoch:1, Step:7740, Discriminator Loss:2.2766993045806885, Generator Loss:0.17307129502296448
Epoch:1, Step:7750, Discriminator Loss:0.8330323696136475, Generator Loss:1.784219741821289
Epoch:1, Step:7760, Discriminator Loss:0.7858624458312988, Generator Loss:1.5796871185302734
Epoch:1, Step:7770, Discriminator Loss:0.5875513553619385, Generator Loss:2.4123587608337402
Epoch:1, Step:7780, Discriminator Loss:1.3635058403015137, Generator Loss:0.5176557898521423
Epoch:1, Step:7790, Discriminator Loss:0.8231273889541626, Generator Loss:1.362342357635498
Epoch:1, Step:7800, Discriminator Loss:1.0299710035324097, Generator Loss:0.8586621284484863
Epoch:1, Step:7810, Discriminator Loss:1.3420755863189697, Generator Loss:0.618529736995697
Epoch:1, Step:7820, Discriminator Loss:1.7428454160690308, Generator Loss:3.1821365356445312
Epoch:1, Step:7830, Discriminator Loss:0.9702734351158142, Generator Loss:0.836617648601532
Epoch:1, Step:7840, Discriminator Loss:0.7201659679412842, Generator Loss:1.3264243602752686
Epoch:1, Step:7850, Discriminator Loss:0.5791782140731812, Generator Loss:1.6338837146759033
Epoch:1, Step:7860, Discriminator Loss:2.4935574531555176, Generator Loss:0.154000923037529
Epoch:1, Step:7870, Discriminator Loss:0.7942473888397217, Generator Loss:1.6636710166931152
Epoch:1, Step:7880, Discriminator Loss:1.2190587520599365, Generator Loss:0.6767886877059937
Epoch:1, Step:7890, Discriminator Loss:1.4882806539535522, Generator Loss:0.7722025513648987
Epoch:1, Step:7900, Discriminator Loss:0.8169695138931274, Generator Loss:1.1128408908843994
Epoch:1, Step:7910, Discriminator Loss:1.128216028213501, Generator Loss:1.0483663082122803
Epoch:1, Step:7920, Discriminator Loss:1.1422812938690186, Generator Loss:0.8119423985481262
Epoch:1, Step:7930, Discriminator Loss:1.772120475769043, Generator Loss:0.3084484338760376
Epoch:1, Step:7940, Discriminator Loss:1.064334750175476, Generator Loss:1.20781672000885
Epoch:1, Step:7950, Discriminator Loss:1.1031100749969482, Generator Loss:0.8679174184799194
Epoch:1, Step:7960, Discriminator Loss:1.5191848278045654, Generator Loss:0.4072871506214142
Epoch:1, Step:7970, Discriminator Loss:1.3849964141845703, Generator Loss:0.5116239786148071
Epoch:1, Step:7980, Discriminator Loss:1.571623682975769, Generator Loss:0.4037841260433197
Epoch:1, Step:7990, Discriminator Loss:1.3423773050308228, Generator Loss:0.5301346778869629
Epoch:1, Step:8000, Discriminator Loss:1.1522276401519775, Generator Loss:0.6867524981498718
Epoch:1, Step:8010, Discriminator Loss:1.4903345108032227, Generator Loss:0.45340293645858765
Epoch:1, Step:8020, Discriminator Loss:1.7643167972564697, Generator Loss:1.8916078805923462
Epoch:1, Step:8030, Discriminator Loss:1.409109354019165, Generator Loss:0.5400325655937195
Epoch:1, Step:8040, Discriminator Loss:0.5668800473213196, Generator Loss:1.7341457605361938
Epoch:1, Step:8050, Discriminator Loss:0.8758357763290405, Generator Loss:1.2924926280975342
Epoch:1, Step:8060, Discriminator Loss:0.6286016702651978, Generator Loss:1.6313952207565308
Epoch:1, Step:8070, Discriminator Loss:1.5330355167388916, Generator Loss:0.4080914258956909
Epoch:1, Step:8080, Discriminator Loss:1.651624083518982, Generator Loss:0.3565288484096527
Epoch:1, Step:8090, Discriminator Loss:1.4840840101242065, Generator Loss:1.731472134590149
Epoch:1, Step:8100, Discriminator Loss:1.346005916595459, Generator Loss:0.9164153337478638

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.